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(0 is transparent & 1 is opaque) + max: 1 + min: 0 + name: image_darken + required: false + step: 0.1 + value_type: float + widget: number + label: Background + name: background + required: false + widget: object + label: Widget Style + name: design + required: false + widget: object + - label: Body + name: body + required: false + widget: markdown + folder: content/home + identifier_field: widget_id + label: Homepage + media_folder: /assets/media + name: home + path: '{{slug}}' + public_folder: "" + summary: '{{filename}}: {{title}}' +- create: true + fields: + - label: Display name (such as your full name) + name: title + widget: string + - label: Position or tagline (such as Professor of AI) + name: role + required: false + widget: string + - default: avatar + label: Avatar (upload an image named `avatar.jpg/png`) + media_library: + config: + multiple: false + name: avatar_filename + required: false + widget: image + - label: Short biography (shown in author boxes) + name: bio + required: false + widget: string + - label: Full biography (shown in About widget) + name: body + required: false + widget: markdown + - label: Interests (shown in About widget) + name: interests + required: false + widget: list + - fields: + - label: Link + name: link + widget: string + - label: Icon pack + multiple: false + name: icon_pack + options: + - label: None + value: "" + - label: Solid + value: fas + - label: Regular + value: far + - label: Brand + value: fab + - label: Academic + value: ai + widget: select + - label: Icon (see https://wowchemy.com/docs/page-builder/#icons) + name: icon + widget: string + - label: Label (tooltip) + name: label + required: false + widget: string + - fields: + - default: false + label: Header (main menu) + name: header + required: false + widget: boolean + label: Display in About widget and... + name: display + widget: object + label: Social links + name: social + required: false + widget: list + - fields: + - label: Organization + name: name + required: true + widget: string + - label: Link + name: url + required: false + widget: string + label: Organizations you belong to or are affiliated with (shown in About widget) + name: organizations + required: false + widget: list + - fields: + - fields: + - label: Course + name: course + required: true + widget: string + - label: Institution + name: institution + required: true + widget: string + - label: Year + name: year + required: false + value_type: int + widget: number + label: Courses + name: courses + required: false + widget: list + label: Education + name: education + required: false + widget: object + - label: Email (to use a Gravatar.com avatar) + name: email + required: false + widget: string + - default: false + label: Super user (is this the primary site user?) + name: superuser + widget: boolean + - label: User groups (only for organization websites) + name: user_groups + required: false + widget: list + filter: + field: cms_exclude + folder: content/authors + label: Authors + label_singular: Author + name: authors + path: '{{slug}}/_index' +- create: true + fields: + - label: Title + name: title + widget: string + - label: Subtitle + name: subtitle + required: false + widget: string + - label: Body + name: body + widget: markdown + - label: Publish this page on + name: date + widget: datetime + - label: Summary + name: summary + required: false + widget: markdown + - default: false + label: Draft + name: draft + required: false + widget: boolean + - default: false + label: Featured + name: featured + required: false + widget: boolean + - label: Authors + name: authors + required: false + widget: list + - label: Tags + name: tags + required: false + widget: list + - label: Categories + name: categories + required: false + widget: list + - label: Projects + name: projects + required: false + widget: list + - fields: + - default: featured + label: Upload an image named `featured.jpg/png` + media_library: + config: + multiple: false + name: filename + required: false + widget: image + - label: Caption + name: caption + required: false + widget: string + - label: Description for screen readers + name: alt_text + required: false + widget: string + - default: Smart + label: Where's the focal point in the image? Smart, Center, TopLeft, Top, TopRight, + Left, Right, BottomLeft, Bottom, BottomRight. + name: focal_point + required: false + widget: string + - default: false + label: Thumbnail Only? + name: preview_only + required: false + widget: boolean + label: Featured Image + name: image + required: false + widget: object + filter: + field: cms_exclude + folder: content/post + label: Posts + label_singular: Post + name: posts + path: '{{slug}}/index' +- create: true + fields: + - label: Title + name: title + widget: string + - label: Subtitle + name: subtitle + required: false + widget: string + - label: Body + name: body + required: false + widget: markdown + - label: Publish this page on + name: date + widget: datetime + - label: Summary + name: summary + required: false + widget: markdown + - default: false + label: Draft + name: draft + required: false + widget: boolean + - default: false + label: Featured + name: featured + required: false + widget: boolean + - label: Authors + name: authors + required: false + widget: list + - label: Tags + name: tags + required: false + widget: list + - label: Categories + name: categories + required: false + widget: list + - label: External link (optional - replaces link to project page) + name: external_link + required: false + widget: string + - fields: + - label: Link + name: url + widget: string + - label: Link text + name: name + required: false + widget: string + - label: Icon pack + multiple: false + name: icon_pack + options: + - label: None + value: "" + - label: Solid + value: fas + - label: Regular + value: far + - label: Brand + value: fab + - label: Academic + value: ai + required: false + widget: select + - label: Icon (see https://wowchemy.com/docs/page-builder/#icons) + name: icon + required: false + widget: string + label: Links + name: links + required: false + widget: list + - fields: + - default: featured + label: Upload an image named `featured.jpg/png` + media_library: + config: + multiple: false + name: filename + required: false + widget: image + - label: Caption + name: caption + required: false + widget: string + - label: Description for screen readers + name: alt_text + required: false + widget: string + - default: Smart + label: Where's the focal point in the image? Smart, Center, TopLeft, Top, TopRight, + Left, Right, BottomLeft, Bottom, BottomRight. + name: focal_point + required: false + widget: string + - default: false + label: Thumbnail Only? + name: preview_only + required: false + widget: boolean + label: Featured Image + name: image + required: false + widget: object + filter: + field: cms_exclude + folder: content/project + label: Projects + label_singular: Project + name: projects + path: '{{slug}}/index' +- create: true + fields: + - label: Title + name: title + widget: string + - label: Abstract + name: abstract + widget: text + - label: Where + name: location + widget: text + - label: From + name: date + widget: datetime + - default: "" + label: To + name: date_end + widget: datetime + - default: false + label: All day event? + name: all_day + widget: boolean + - fields: + - label: Link + name: url + widget: string + - label: Link text + name: name + required: false + widget: string + - label: Icon pack + multiple: false + name: icon_pack + options: + - label: None + value: "" + - label: Solid + value: fas + - label: Regular + value: far + - label: Brand + value: fab + - label: Academic + value: ai + required: false + widget: select + - label: Icon (see https://wowchemy.com/docs/page-builder/#icons) + name: icon + required: false + widget: string + label: Links/Tickets + name: links + required: false + widget: list + - label: Event + name: event + widget: string + - label: Event link + name: event_url + widget: string + - label: Publish this page on + name: publishDate + widget: datetime + - label: Markdown slides (reference a deck in 'content/slides/') + name: slides + required: false + widget: string + - default: false + label: Draft + name: draft + required: false + widget: boolean + - default: false + label: Featured + name: featured + required: false + widget: boolean + - label: Authors + name: authors + required: false + widget: list + - label: Tags + name: tags + required: false + widget: list + - label: Categories + name: categories + required: false + widget: list + - label: Projects (reference projects in 'content/project/') + name: projects + required: false + widget: list + - fields: + - default: featured + label: Upload an image named `featured.jpg/png` + media_library: + config: + multiple: false + name: filename + required: false + widget: image + - label: Caption + name: caption + required: false + widget: string + - label: Description for screen readers + name: alt_text + required: false + widget: string + - default: Smart + label: Where's the focal point in the image? Smart, Center, TopLeft, Top, TopRight, + Left, Right, BottomLeft, Bottom, BottomRight. + name: focal_point + required: false + widget: string + - default: false + label: Thumbnail Only? + name: preview_only + required: false + widget: boolean + label: Featured Image + name: image + required: false + widget: object + - label: Details + name: body + required: false + widget: markdown + filter: + field: cms_exclude + folder: content/event + label: Events + label_singular: Event + name: events + path: '{{slug}}/index' +- create: true + fields: + - label: Title + name: title + widget: string + - label: Subtitle + name: subtitle + required: false + widget: string + - default: + - "0" + label: Publication type + multiple: true + name: publication_types + options: + - label: Uncategorized + value: "0" + - label: Conference paper + value: "1" + - label: Journal article + value: "2" + - label: Preprint / Working Paper + value: "3" + - label: Report + value: "4" + - label: Book + value: "5" + - label: Book section + value: "6" + - label: Thesis + value: "7" + - label: Patent + value: "8" + required: true + widget: select + - label: Authors + name: authors + required: true + widget: list + - label: Author Notes (contributions or affiliations for each author) + name: author_notes + required: false + widget: list + - label: DOI + name: doi + required: false + widget: string + - label: Publication + name: publication + required: false + widget: string + - label: Publication (abbreviated) + name: publication_short + required: false + widget: string + - label: Abstract + name: abstract + required: false + widget: text + - default: false + label: Draft + name: draft + required: false + widget: boolean + - default: false + label: Featured + name: featured + required: false + widget: boolean + - label: Tags + name: tags + required: false + widget: list + - label: Categories + name: categories + required: false + widget: list + - label: Projects + name: projects + required: false + widget: list + - label: Markdown slides (reference a deck in 'content/slides/') + name: slides + required: false + widget: string + - fields: + - default: featured + label: Upload an image named `featured.jpg/png` + media_library: + config: + multiple: false + name: filename + required: false + widget: image + - label: Caption + name: caption + required: false + widget: string + - label: Description for screen readers + name: alt_text + required: false + widget: string + - default: Smart + label: Where's the focal point in the image? Smart, Center, TopLeft, Top, TopRight, + Left, Right, BottomLeft, Bottom, BottomRight. + name: focal_point + required: false + widget: string + - default: false + label: Thumbnail Only? + name: preview_only + required: false + widget: boolean + label: Featured Image + name: image + required: false + widget: object + - label: Summary (shortened abstract) + name: summary + required: false + widget: text + - label: Details + name: body + required: false + widget: markdown + - label: Publish this page on + name: date + widget: datetime + filter: + field: cms_exclude + folder: content/publication + label: Publications + label_singular: Publication + name: publications + path: '{{slug}}/index' +- create: true + fields: + - label: Title + name: title + widget: string + - label: Slides (separate with `---`) + name: body + widget: markdown + - label: Publish on + name: date + widget: datetime + - label: Summary + name: summary + required: false + widget: text + - default: false + label: Draft + name: draft + required: false + widget: boolean + - label: Tags + name: tags + required: false + widget: list + - fields: + - default: black + label: Theme (see https://github.com/hakimel/reveal.js#theming) + name: theme + required: false + widget: string + label: Slide options + name: slides + required: false + widget: object + - fields: + - default: featured + label: Upload an image named `featured.jpg/png` + media_library: + config: + multiple: false + name: filename + required: false + widget: image + - label: Caption + name: caption + required: false + widget: string + - label: Description for screen readers + name: alt_text + required: false + widget: string + - default: Smart + label: Where's the focal point in the image? Smart, Center, TopLeft, Top, TopRight, + Left, Right, BottomLeft, Bottom, BottomRight. + name: focal_point + required: false + widget: string + - default: false + label: Thumbnail Only? + name: preview_only + required: false + widget: boolean + label: Featured Image + name: image + required: false + widget: object + filter: + field: cms_exclude + folder: content/slides + label: Slides + label_singular: Slides + name: slides + path: '{{slug}}/index' +- files: + - fields: + - label: Title + name: title + widget: string + - label: Publish Date + name: date + widget: datetime + - label: Subtitle + name: subtitle + required: false + widget: string + - label: Summary + name: summary + required: false + widget: markdown + - default: false + label: Draft + name: draft + required: false + widget: boolean + - label: Body + name: body + widget: markdown + file: content/privacy.md + label: Privacy Policy + name: privacy + - fields: + - label: Title + name: title + widget: string + - label: Publish Date + name: date + widget: datetime + - label: Subtitle + name: subtitle + required: false + widget: string + - label: Summary + name: summary + required: false + widget: markdown + - default: false + label: Draft + name: draft + required: false + widget: boolean + - label: Body + name: body + widget: markdown + file: content/terms.md + label: Terms + name: terms + label: Pages + name: pages +local_backend: false +media_folder: assets/media +public_folder: /media diff --git a/admin/index.html b/admin/index.html new file mode 100644 index 0000000..a1066a4 --- /dev/null +++ b/admin/index.html @@ -0,0 +1,15 @@ + + + + + + + Wowchemy CMS + + + + + + + + diff --git a/author/alejandro-f-frangi/index.html b/author/alejandro-f-frangi/index.html new file mode 100644 index 0000000..3e59dc0 --- /dev/null +++ b/author/alejandro-f-frangi/index.html @@ -0,0 +1,1005 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Alejandro F Frangi | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Andrew P. King

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Latest

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Andy D Castellano-Smith

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Anna Reithmeir

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PhD Student

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+ + Technical University of Munich + +

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Anna Reithmeir is a PhD student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her B.Sc. and M.Sc. in Informatics from TU Munich with a focus on computer vision and high performance computing. In her Master’s thesis at the Munich Institute for Robotics and Machine Intelligence (MIRMI), she developed a novel algorithm for human-robot manipulability domain adaptation. Her research interests lie in the analysis and development of robust and data-driven models for image registration, numerics of machine learning algorithms and Riemannian manifolds.

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Interests
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  • Image Registration
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  • Physics-Inspired Regularization
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  • Manifold-Valued Data
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Education
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    M.Sc. in Informatics, 2022

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    TU Munich

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    B.Sc. in Informatics, 2019

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    TU Munich

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Student Projects & Theses
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    Exploring SPD Feature Descriptors for Medical Image Classification, Master's Thesis

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    open, 1.1.2024 | tba

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Anneliese Riess

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PhD Student

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+ + Technical University of Munich + +

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+ + Helmholtz Center Munich + +

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Anneliese Riess is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University Munich (TUM). She received her B.Sc. and M.Sc. in Mathematics at TUM and devoted a substantial part of her studies to the field of probability theory. In her Master’s thesis she investigated Majority Voting Processes, a class of interacting particle systems. The main focus of the thesis was the equilibrium behaviour of such stochastic models. Prior to her PhD, she worked on two different projects at the university in her final year of her Master’s degree. In the first project, she worked on creating a model that describes the behaviour of DNA methylation. The second project involved modelling and analysing the propagation of underground water. Her research interests lie in the mathematical foundations of privacy-preserving artificial intelligence.

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Interests
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  • Mathematical Foundations of Privacy Preserving AI
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  • Probability Theory
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Education
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    M.Sc. in Mathematics, 2023

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    TU Munich

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    B.Sc. in Mathematics, 2019

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    TU Munich

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Aurelien Bustin

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Latest

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Bartłomiej W. Papież

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Bram Ruijsink

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Charles L Truwit

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Christine Preibisch

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Claude Comtat

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Claudia Prieto

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Cosmin I. Bercea

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PhD Student

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+ + Technical University of Munich + +

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+ + Helmholtz Center Munich + +

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Cosmin Bercea is a Ph.D. Student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University of Munich (TUM). He received his B.Sc. and M.Sc. in Computer Science from the FAU University in Erlangen, Germany, focusing on pattern recognition and medical image analysis. In his Master’s thesis at Siemens Healthineers in Erlangen, he built novel shared memory neural networks for medical imaging. Before his Ph.D., he worked as a research engineer at Bosch Corporate Research, developing deep learning algorithms for scene understanding for self-driving cars. His research interests lie in interpretable machine learning algorithms for anomaly detection.

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Interests
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  • Generative AI
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  • Unsupervised Representation Learning
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  • Anomaly Detection
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Education
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    M.Sc. in Computer Science, 2018

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    FAU Erlangen

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    B.Sc. in Computer Science, 2015

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    FAU Erlangen

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Student Projects & Theses
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    Diffusion Models for Counterfactual Pathology Synthesis, Master's Thesis

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    running, 1.11.2023 | Malek Ben Alaya

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    Diffusion Models for Fetal US Anomaly Detection, GRP

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    running, 1.11.2023 | Hanna Mykula

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    Unsupervised Representation Learning for Alzheimer’s Disease Quantification, GRP

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    running, 1.10.2023 | Mehmet Yigit Avci

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    Unsupervised Representation Learning for Alzheimer’s Disease Detection, GRP

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    finished, 31.10.2023 | Mehmet Yigit Avci

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    Diffusion Models for Unsupervised Anomaly Detection, GRP

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    finished, 24.10.2023 | Michael Neumayr

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    Unsupervised Anomaly Detection in Fetal Brain Ultrasound, Master's Thesis

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    finished, 15.08.2023 | Ruxandra Petrescu

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Latest

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Bercea + https://compai-lab.io/author/cosmin-i.-bercea/ + + + + Five papers accepted at MICCAI 2023 workshops + https://compai-lab.io/post/iml_miccai_workshops/ + Thu, 14 Sep 2023 00:00:00 +0000 + https://compai-lab.io/post/iml_miccai_workshops/ + <p>Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p>Interested to hear more about our work? Then join us at the following workshops:</p> +<ul> +<li> +<p>Veronika Spieker will be at the <a href="https://dgm4miccai.github.io/" target="_blank" rel="noopener">DGM4</a> workshop to talk about <a href="https://arxiv.org/abs/2308.08830" target="_blank" rel="noopener">Neural Implicit Representations for Abdominal MR Reconstruction</a> on October 8, at 10:25.</p> +</li> +<li> +<p>Hannah Eichhorn presents her work on physics-aware motion simulation for T2*-weighted MRI at the <a href="https://2023.sashimi-workshop.org/program/" target="_blank" rel="noopener">SASHIMI</a> workshop on October 8, at 14:40. Check out the <a href="https://arxiv.org/abs/2303.10987" target="_blank" rel="noopener">preprint</a> for more information!</p> +</li> +<li> +<p>Maxime Di Folco presents at the <a href="https://stacom.github.io/stacom2023/" target="_blank" rel="noopener">STACOM</a> workshop on October 12, at 11:15 the work of Josh Stein on &ldquo;Sparse annotation strategies for segmentation of short axis cardiac MRI&rdquo; (<a href="https://arxiv.org/abs/2307.12619" target="_blank" rel="noopener">preprint</a>).</p> +</li> +<li> +<p>Cosmin Bercea will talk about <a href="https://arxiv.org/pdf/2308.13861.pdf" target="_blank" rel="noopener">Bias in Unsupervised Anomaly Detection</a> at the <a href="https://faimi-workshop.github.io/2023-miccai/" target="_blank" rel="noopener">FAIMI</a> workshop on October 12, at 2:50 PDT.</p> +</li> +<li> +<p>Daniel Lang will talk about <a href="https://arxiv.org/abs/2303.05861" target="_blank" rel="noopener">Anomaly Detection in Non-Contrast Enhanced Breast MRI</a> at the <a href="https://caption-workshop.github.io/miccai2023/#Workshop%20sessions" target="_blank" rel="noopener">CaPTion</a> workshop on October 12.</p> +</li> +</ul> + + + + Two papers accepted at MICCAI 2023 + https://compai-lab.io/post/bercea_miccai/ + Fri, 26 May 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_miccai/ + <p>&ldquo;<em>What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection</em> and <em>Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection</em> by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/morphaeus.gif" alt="MorphAEus" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<ul> +<li>Curios what auto-encoders actually learn? Check out <a href="https://ci.bercea.net/project/morphaeus/" target="_blank" rel="noopener">this</a> project page to find out more.</li> +</ul> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/phanes.gif" alt="PHANES" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<ul> +<li>How can we reverse anomalies in medical images? Check out the project <a href="https://ci.bercea.net/project/phanes/" target="_blank" rel="noopener">here</a>.</li> +</ul> + + + + Paper accepted at ICML IMLH 2023 + https://compai-lab.io/post/bercea_icml/ + Thu, 25 May 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_icml/ + <p>We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!</p> +<p>Curious about how to solve the noise paradox illustrated below? Check out our <a href="https://ci.bercea.net/project/autoddpm/" target="_blank" rel="noopener">project page</a>.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/noise_paradox.gif" alt="AutoDDPM" loading="lazy" data-zoomable /></div> + </div></figure> +</p> + + + + Paper accepted at MIDL 2023 (oral talk) + https://compai-lab.io/post/bercea_midl/ + Fri, 28 Apr 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_midl/ + <p>&ldquo;<em>Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening</em>&rdquo; by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/ra.png" alt="RA" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>Moving beyond hyperintensity thresholding: This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection. Our proposed method RA outperformed SOTA methods on T1w brain MRIs, detecting more global anomalies (AUROC increased from 73.1 to 89.4) and local pathologies (detection rate increased from 52.6% to 86.0%).</p> +<p>Want to know more? Check the <a href="https://ci.bercea.net/project/ra/" target="_blank" rel="noopener">project site</a>.</p> + + + + New publication at Nature Machine Intelligence + https://compai-lab.io/post/bercea_nature/ + Tue, 02 Aug 2022 00:00:00 +0000 + https://compai-lab.io/post/bercea_nature/ + <p><em>Federated disentangled representation learning for unsupervised brain anomaly detection</em> by Cosmin I. Bercea et al. has been published at Nature Machine Intelligence.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/feddis.png" alt="Feddis" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>In this work, a federated algorithm was trained on more than 1,500 MR scans of healthy study participants from four institutions while maintaining data privacy with the goal to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before.</p> +<p>Check the <a href="https://ci.bercea.net/project/feddis/" target="_blank" rel="noopener">project site</a> for more information.</p> + + + + What do we learn? Debunking the Myth of Unsupervised Outlier Detection + https://compai-lab.io/publication/bercea-2022-we/ + Wed, 08 Jun 2022 00:00:00 +0000 + https://compai-lab.io/publication/bercea-2022-we/ + <div class="alert alert-note"> + <div> + Click the <em>Cite</em> button above to demo the feature to enable visitors to import publication metadata into their reference management software. + </div> +</div> + + + + + diff --git a/author/daniel-m.-lang/avatar.png b/author/daniel-m.-lang/avatar.png new file mode 100644 index 0000000..92246a6 Binary files /dev/null and b/author/daniel-m.-lang/avatar.png differ diff --git a/author/daniel-m.-lang/avatar_hu30628bf96a1bbbf825de70e7e388d37a_614916_270x270_fill_lanczos_center_3.png b/author/daniel-m.-lang/avatar_hu30628bf96a1bbbf825de70e7e388d37a_614916_270x270_fill_lanczos_center_3.png new file mode 100644 index 0000000..85abcbf Binary files /dev/null and b/author/daniel-m.-lang/avatar_hu30628bf96a1bbbf825de70e7e388d37a_614916_270x270_fill_lanczos_center_3.png differ diff --git a/author/daniel-m.-lang/index.html b/author/daniel-m.-lang/index.html new file mode 100644 index 0000000..d6c702d --- /dev/null +++ b/author/daniel-m.-lang/index.html @@ -0,0 +1,1192 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Daniel M. Lang | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Daniel M. Lang

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Daniel Lang will be a postdoc at the Institute of Machine Learning in Biomedical Imaging at Helmholtz Munich. +His research interest focuses on the application of deep learning models for problem settings in the field of +medical imaging with a special focus on cancer management. +He is particularly interested in topics like transfer and selfsupervised learning, out of distribution problems and +domain adaptation, and survival analysis.

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Education
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    PhD in Physics, 2022

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    Helmholtz Munich and Technical University Munich

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    MSc in Physics, 2018

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    University Regensburg

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    BSc in Physics, 2016

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    University Regensburg

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Daniel Rueckert

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Latest

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Devran Ugurlu

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Dimitrios C. Karampinos

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+ + + + Emily Chan + + +
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Emily Chan

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Research Scientist

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+ + Helmholtz Center Munich + +

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+ + King's College London + +

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Emily Chan is a postdoctoral researcher at the Institute of Machine Learning for Biomedical Imaging at Helmholtz Munich. She received her PhD in 2022 from King’s College London, where she worked on utilising classical machine learning and deep learning techniques with limited and imbalanced data for MR liver imaging, in collaboration with Perspectum. She is particularly interested in the automation of various clinically-relevant tasks in radiology, with her research at the IML focusing on deep learning for the early diagnosis and prognosis of Alzheimer’s disease.

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Interests
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  • Medical image computing
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  • Transfer learning
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  • Multi-modal learning
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Education
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    PhD in Biomedical Engineering, 2022

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    King's College London

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    MEng in Biomedical Engineering, 2016

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    Imperial College London

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Esther Puyol-Antón

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Latest

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Fergus V Gleeson

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Latest

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Gary Cook

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Latest

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Gastao Cruz

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Latest

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+ + + + Georgios Kaissis + + +
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Georgios Kaissis

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Principal Investigator

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+ + Helmholtz Center Munich + +

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Georgios Kaissis is a principal investigator at the Institute of Biomedical Machine Learning (IML) at the Helmholtz Center Munich, a senior research scientist at the Institute of Artificial Intelligence and Informatics in Medicine and specialist diagnostic radiologist at the Institute for Radiology at TUM, a postdoctoral researcher at the Department of Computing at Imperial College London and leads the Healthcare Unit at OpenMined. His research concentrates on biomedical image analysis with a focus on next-generation privacy-preserving machine learning methods as well as probabilistic methods for the design and deployment of robust, secure, fair and transparent machine learning algorithms to medical imaging workflows.

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Interests
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  • Reliable artificial intelligence
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  • Medical image computing
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  • Probabilistic methods
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Education
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    PostDoc in AI for medical imaging

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    Imperial College London, UK

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    Specialist Radiologist

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    Technical University of Munich, Germany

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    Doctorate in molecular medicine and systems biology (Dr. med.)

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    LMU Munich, Germany

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    Medical Degree

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    LMU Munich, Germany

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    Master of Healthcare Business Administration

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    FAU Erlangen-Nuremberg, Germany

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Graeme P Penney

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Haiying Liu

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Hannah Eichhorn

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PhD student

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Hannah Eichhorn is a PhD student at the Institute of Machine Learning in Biomedical Imaging (IML), Helmholtz Munich. She received her B.Sc. in Physics from Heidelberg University and her M.Sc. in Bio- and Medical Physics from University of Copenhagen. In her Master’s thesis at the Neurobiology Research Unit, Copenhagen University Hospital, she worked on prospective motion correction for brain magnetic resonance imaging (MRI). Her doctoral research focuses on deep-learning based reconstruction and motion correction of multi-parametric brain MRI, in collaboration with the Neuroscientific MR-Physics research group at Klinikum rechts der Isar (TUM).

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Interests
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  • Brain Magnetic Resonance Imaging
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  • Image reconstruction & Motion Correction
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  • Deep learning
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Education
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    MSc in Bio- and Medical Physics, 2021

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    Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

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    BSc in Physics, 2018

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    Heidelberg University, Heidelberg, Germany

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Check out <a href="https://ieeexplore.ieee.org/document/10285512" target="_blank" rel="noopener">this review</a>, which identifies differences and synergies of recent methods and bridges the gap between AI and MR physics.</p> + + + + Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + Fri, 13 Oct 2023 00:00:00 +0000 + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + + + + + Five papers accepted at MICCAI 2023 workshops + https://compai-lab.io/post/iml_miccai_workshops/ + Thu, 14 Sep 2023 00:00:00 +0000 + https://compai-lab.io/post/iml_miccai_workshops/ + <p>Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p>Interested to hear more about our work? Then join us at the following workshops:</p> +<ul> +<li> +<p>Veronika Spieker will be at the <a href="https://dgm4miccai.github.io/" target="_blank" rel="noopener">DGM4</a> workshop to talk about <a href="https://arxiv.org/abs/2308.08830" target="_blank" rel="noopener">Neural Implicit Representations for Abdominal MR Reconstruction</a> on October 8, at 10:25.</p> +</li> +<li> +<p>Hannah Eichhorn presents her work on physics-aware motion simulation for T2*-weighted MRI at the <a href="https://2023.sashimi-workshop.org/program/" target="_blank" rel="noopener">SASHIMI</a> workshop on October 8, at 14:40. Check out the <a href="https://arxiv.org/abs/2303.10987" target="_blank" rel="noopener">preprint</a> for more information!</p> +</li> +<li> +<p>Maxime Di Folco presents at the <a href="https://stacom.github.io/stacom2023/" target="_blank" rel="noopener">STACOM</a> workshop on October 12, at 11:15 the work of Josh Stein on &ldquo;Sparse annotation strategies for segmentation of short axis cardiac MRI&rdquo; (<a href="https://arxiv.org/abs/2307.12619" target="_blank" rel="noopener">preprint</a>).</p> +</li> +<li> +<p>Cosmin Bercea will talk about <a href="https://arxiv.org/pdf/2308.13861.pdf" target="_blank" rel="noopener">Bias in Unsupervised Anomaly Detection</a> at the <a href="https://faimi-workshop.github.io/2023-miccai/" target="_blank" rel="noopener">FAIMI</a> workshop on October 12, at 2:50 PDT.</p> +</li> +<li> +<p>Daniel Lang will talk about <a href="https://arxiv.org/abs/2303.05861" target="_blank" rel="noopener">Anomaly Detection in Non-Contrast Enhanced Breast MRI</a> at the <a href="https://caption-workshop.github.io/miccai2023/#Workshop%20sessions" target="_blank" rel="noopener">CaPTion</a> workshop on October 12.</p> +</li> +</ul> + + + + Abstracts accepted at 2023 ISMRM & ISMRT Annual Meeting + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + Tue, 25 Apr 2023 00:00:00 +0000 + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + <p>Veronika Spieker&rsquo;s and Hannah Eichhorn&rsquo;s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM &amp; ISMRT Annual Meeting.</p> +<p>Veronika Spieker will present her work on &ldquo;<em>Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction</em>&rdquo; on Monday, 05 June 2023 at 1:45 pm EDT.</p> +<p>Hannah Eichhorn will present her work on &ldquo;<em>Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations</em>&rdquo; on Tuesday, 06 June 2023 at 4:45 pm EDT. Check <a href="https://github.com/HannahEichhorn/T2starRealisticMotionSimulation" target="_blank" rel="noopener">this GitHub repository</a> for more information.</p> + + + + + diff --git a/author/ihsane-olakorede/index.html b/author/ihsane-olakorede/index.html new file mode 100644 index 0000000..7bfe9a9 --- /dev/null +++ b/author/ihsane-olakorede/index.html @@ -0,0 +1,1005 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Ihsane Olakorede | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Ihsane Olakorede

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Ilkay Oksuz

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Inês P Machado

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Irène Buvat

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Latest

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James R Clough

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James R. Clough

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Latest

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Jane M Blackall

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Johannes Kiechle

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PhD Student

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+ + Technical University of Munich + +

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+ + MRI TUM Munich + +

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Johannes Kiechle is a Ph.D. Student at the Technical University of Munich. He received his B.Eng. from Munich University of Applied Sciences and M.Sc. from Technical University of Munich. In his Master’s thesis, he investigated the shape change of the human hippocampus in the course of ageing within a population of healthy individuals using graph neural networks. For his PhD project, he works in collaboration with the department of Radiation Oncology at the University Hospital rechts der Isar. Therein the focus is on the development and validation of histology-specific AI-based decision support systems for soft-tissue-sarcoma patients.

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Interests
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  • Shape Analysis
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  • Representation Learning
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  • Magnetic Resonance Imaging
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Education
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    M.Sc. in Electrical and Computer Engineering, 2023

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    Technical University of Munich

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    B.Eng. in Electrical Engineering and Information Technology, 2020

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    Munich University of Applied Sciences

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Julia A. Schnabel

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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging

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+ + Technical University Munich + +

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+ + Helmholtz Center Munich + +

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+ + King's College London + +

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Julia A. Schnabel is Professor of Computational Imaging and AI in Medicine at Technical University of Munich (TUM Liesel Beckmann Distinguished Professorship) and Director of a new Institute of Machine Learning in Biomedical Imaging at Helmholtz Center Munich (Helmholtz Distinguished Professorship), with secondary appointment as Chair in Computational Imaging at King’s College London. She graduated in Computer Science (equiv. MSc) from Technical University of Berlin, Berlin, Germany, and was awarded the PhD in Computer Science from University College London, UK. In 2007, she joined the University of Oxford, UK as Associate Professor in Engineering Science (Medical Imaging), where she became Full Professor of Engineering Science by Recognition of Distinction in 2014. She joined King’s College London as a new Chair in 2015, and in 2021 joined TUM and Helmholtz Munich for her current positions. Her research interests include machine/deep learning, nonlinear motion modeling, as well as multimodality and quantitative imaging, for cancer imaging, cardiac imaging, neuroimaging and perinatal imaging. Dr. Schnabel has been elected Fellow of IEEE (2021), Fellow of ELLIS (2019), and Fellow of the MICCAI Society (2018). She is an Associate Editor of the IEEE Transactions on Medical Imaging on whose steering board she serves since 2021, the IEEE Transactions of Biomedical Engineering, on the Editorial Board of Medical Image Analysis and Executive/Founding Editor of MELBA. She currently serves as elected Technical Representative on IEEE EMBS AdCom, as voting member of the IEEE EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP), as Executive Secretary to the MICCAI board, and as member of ELLIS Health and ELLIS Munich.

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Interests
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  • Biomedical Imaging
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  • Artificial Intelligence in Medicine
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Education
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    PhD in Computer Science, 1998

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    University College London

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    Diplom (Msc. eq) in Computer Science, 1993

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    Technical University of Berlin

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Latest

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Schnabel + https://compai-lab.io/author/julia-a.-schnabel/ + + + + Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + Fri, 13 Oct 2023 00:00:00 +0000 + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + + + + + What do we learn? Debunking the Myth of Unsupervised Outlier Detection + https://compai-lab.io/publication/bercea-2022-we/ + Wed, 08 Jun 2022 00:00:00 +0000 + https://compai-lab.io/publication/bercea-2022-we/ + <div class="alert alert-note"> + <div> + Click the <em>Cite</em> button above to demo the feature to enable visitors to import publication metadata into their reference management software. + </div> +</div> + + + + + AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information + https://compai-lab.io/publication/li-2022-atrialjsqnet/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/publication/li-2022-atrialjsqnet/ + + + + + Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning + https://compai-lab.io/publication/dal-2022-improved/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/publication/dal-2022-improved/ + + + + + Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation + https://compai-lab.io/fpublications/pmid-32746141/ + Tue, 01 Dec 2020 00:00:00 +0000 + https://compai-lab.io/fpublications/pmid-32746141/ + + + + + Model-Based and Data-Driven Strategies in Medical Image Computing + https://compai-lab.io/fpublications/8867900/ + Wed, 01 Jan 2020 00:00:00 +0000 + https://compai-lab.io/fpublications/8867900/ + + + + + A topological loss function for deep-learning based image segmentation using persistent homology + https://compai-lab.io/fpublications/clough-2019-topological/ + Tue, 01 Jan 2019 00:00:00 +0000 + https://compai-lab.io/fpublications/clough-2019-topological/ + + + + + Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning + https://compai-lab.io/fpublications/oksuz-2019136/ + Tue, 01 Jan 2019 00:00:00 +0000 + https://compai-lab.io/fpublications/oksuz-2019136/ + + + + + Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling + https://compai-lab.io/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/ + Sat, 01 Oct 2016 00:00:00 +0000 + https://compai-lab.io/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/ + + + + + MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration + https://compai-lab.io/fpublications/heinrich-2012-mind/ + Sun, 01 Jan 2012 00:00:00 +0000 + https://compai-lab.io/fpublications/heinrich-2012-mind/ + + + + + Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration + https://compai-lab.io/fpublications/rueckert-2003-automatic/ + Wed, 01 Jan 2003 00:00:00 +0000 + https://compai-lab.io/fpublications/rueckert-2003-automatic/ + + + + + A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations + https://compai-lab.io/fpublications/schnabel-2001-generic/ + Mon, 01 Jan 2001 00:00:00 +0000 + https://compai-lab.io/fpublications/schnabel-2001-generic/ + + + + + diff --git a/author/jun-li/avatar.jpg b/author/jun-li/avatar.jpg new file mode 100644 index 0000000..918f024 Binary files /dev/null and b/author/jun-li/avatar.jpg differ diff --git a/author/jun-li/avatar_hu5504c0dc9d75e72190a64b06972982f3_85248_270x270_fill_q75_lanczos_center.jpg b/author/jun-li/avatar_hu5504c0dc9d75e72190a64b06972982f3_85248_270x270_fill_q75_lanczos_center.jpg new file mode 100644 index 0000000..0867e0d Binary files /dev/null and b/author/jun-li/avatar_hu5504c0dc9d75e72190a64b06972982f3_85248_270x270_fill_q75_lanczos_center.jpg differ diff --git a/author/jun-li/index.html b/author/jun-li/index.html new file mode 100644 index 0000000..032b945 --- /dev/null +++ b/author/jun-li/index.html @@ -0,0 +1,1156 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Jun Li | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Jun Li

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PhD Student

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Jun Li is a Ph.D. Student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her M.E. in Computer Technology from the University of Chinese Academy of Sciences, China. In her Master’s thesis, she developed a novel framework that combines supervised and unsupervised learning for ultrasound report generation. Her research interests lie in Vision and Language, Multi-Modal Learning, and Cross-Modality Generation.

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Interests
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  • Vision and Language
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  • Multi-Modal Learning
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  • Cross-Modality Generation
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Education
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    M.E. in Computer Technology, 2023

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    University of Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology

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    B.E. in Traffic and Transportation, 2020

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    Shenzhen University

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Kerstin Hammernik

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Laura Dal Toso

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Lei Li

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Manav Bhushan

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Marcel Quist

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Mark Jenkinson

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Mattias P Heinrich

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Mattias P. Heinrich

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Maxime Di Folco

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Maxime Di Folco is a PostDoctoral researcher at the Institute of Machine Learning for Biomedical Imaging at Helmholtz Center Munich. His research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods, that aim to acquire low dimensional representation of high dimensional data, with a strong focus on cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.

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Interests
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  • Representation learning
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  • Cardiac imaging
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Education
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    PhD in Artificial Intelligence, 2021

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    Université de Lyon, CREATIS Laboratory

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    MEng in Image Processing, 2018

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    CPE Lyon

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    MSc in Image development and 3D technologies, 2018

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    Université Lyon 1

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Michael Brady

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Miguel Castelo-Branco

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Nicholas Byrne

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others

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Paul K Marsden

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Rene Botnar

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Sabine Franke

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Administrative Assistant

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Sabine Franke supports the Lab for Computational Imaging and AI in Medicine as a member of the administrative staff at the TU campus in Garching. She graduated in 2014 from the University of Graz, Austria, with a degree in conference interpreting for German, English and Spanish. Before joining the team at the TU Munich, she spent several years working as a translator and interpreter in Germany as well as in the Netherlands, adding Dutch to her working languages.

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  • Communication and Relations
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Education
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    Conference Interpreter (MA), 2014

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    University of Graz, Austria

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Sameer Ambekar

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PhD Student

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+ + Technical University of Munich + +

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+ + Helmholtz Center Munich + +

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Sameer Ambekar is a Ph.D. Student at the Technical University of Munich (TUM). He received his Masters in Artificial Intelligence (MSc AI) from the University of Amsterdam (UvA), Netherlands. For his Master’s thesis (48 ECTS), he worked on ‘Test-Time Adaptation for Domain Generalization by generating models and labels through Variational meta-learning’ at the AIM Lab, UvA. Prior to his master’s, he worked as a Research Assistant (RA) at IIT Delhi (IITD) to address Unsupervised Domain Adaptation through methods such as Variational generative latent search. He is interested in solving problems in unsupervised learning through methods such as meta-learning and variational inference alongside learning efficient and transferable features.

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  • Meta Learning
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  • Variational Inference
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Education
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    M.Sc. in Artificial Intelligence (MSc AI), 2023

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    University of Amsterdam, Netherlands

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    B.E. in Computer Science, 2018

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    VTU, India

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Student Projects & Theses
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    tba. Stay tuned on website - https://ambekarsameer.com

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+ + + + Sandra Mayer + + +
+ +

Sandra Mayer

+ +

Administrative Assistant

+ + +

+ + Helmholtz Center Munich + +

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+ + + + +
+

Sandra Mayer supports the Lab for Computational Imaging and AI in Medicine as a member of the administrative staff at the Helmholtz Campus in Neuherberg.

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Interests
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  • Project Management and Administration
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  • Team Management and Support
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  • Communication and Relations
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Education
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+ +

Simona Bottani

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Research Scientist

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+ + Helmholtz Center Munich + +

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Simona Bottani is a PostDoctoral Fellow at the IML where she works on deep learning applied to big research medical imaging cohort. She received her PhD in computer science from Sorbonne University in April 2022. She worked at the ARAMIS Lab and her thesis focused on the application of deep learning models for neuroimaging studies using a large scale clinical data warehouse of the Paris Great Area Hospitals (AP-HP). From 2017 to 2018 she worked as research engineer in the ARAMIS Lab. She received a Master Degree in 2016 and a Bachelor degree in 2014 in Biomedical engineering from Politecnico di Torino.

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Interests
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  • Deep learning
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  • Big data sets
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  • Brain imaging
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Education
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    PhD in Computer Science, 2021

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    Sorbonne Université

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    MSc in Biomedical Engineer, 2016

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    Politecnico di Torino

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    BSc in Biomedical Engineer, 2014

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    Politecnico di Torino

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Sir J. Michael Brady

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Latest

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+ +

Stefan Fischer

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PhD Student

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+ + Technical University of Munich + +

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+ + MRI TUM Munich + +

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Stefan Fischer is a Ph.D. Student at the Technical University of Munich (TUM). He received his B.Sc. and M.Sc. from FAU in Erlangen, Germany with a focus on medical image analysis. In his Master’s thesis at the Radiooncology department of the university hospital Erlangen, he build a generative approach for brain metastasis for data augmentation in MR Imaging. His research interest lies in deep learning based segmentation, transfer learning and curriculum learning.

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Interests
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  • Segmentation
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  • Radiooncology
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  • Transfer Learning/Curriculum Learning
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Education
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    M.Sc. in Computer Science, 2022

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    FAU Erlangen

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    B.Sc. in Medical Engineering, 2019

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    FAU Erlangen

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Tahreema Matin

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Latest

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Thomas Hartkens

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Latest

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Veronika Spieker

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PhD Student

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Veronika Spieker is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Munich and Technical University of Munich (TUM). After completing her B.Sc. in engineering at TU Darmstadt and Virginia Tech, she pursued her interest in the medical domain with a M.Sc. in Medical Technologies at TUM. For her PhD project, she works on Physics-Based AI for Motion Correction in Abdominal MRI in collaboration with the Body Magnetic Resonance Group at the Klinikum rechts der Isar. Her research interests include concepts such as neural implicit representations and it’s application to MR reconstruction and motion estimation.

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Interests
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  • Motion Detection & Correction
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  • Neural Implicit Representations
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Education
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    MSc Medical Technologies and Asstistant Systems, 2021

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    Technical University of Munich

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    MSc Mechanical Engineering, 2021

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    Technical University of Munich

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    BSc Mechanical Engineering, 2017

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    Technical University of Darmstadt / Virginia Tech

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Student Projects & Theses
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    Reducing Labeling Efforts in Segmentation-based Registration in Medical Imaging, Master's thesis

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    finished, 1.11.2023 | Varsha Raveendran

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    Deep Learning For Medical Image Registration, GRP

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    finished, 1.11.2023 | Varsha Raveendran

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Check out <a href="https://ieeexplore.ieee.org/document/10285512" target="_blank" rel="noopener">this review</a>, which identifies differences and synergies of recent methods and bridges the gap between AI and MR physics.</p> + + + + Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + Fri, 13 Oct 2023 00:00:00 +0000 + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + + + + + Five papers accepted at MICCAI 2023 workshops + https://compai-lab.io/post/iml_miccai_workshops/ + Thu, 14 Sep 2023 00:00:00 +0000 + https://compai-lab.io/post/iml_miccai_workshops/ + <p>Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p>Interested to hear more about our work? Then join us at the following workshops:</p> +<ul> +<li> +<p>Veronika Spieker will be at the <a href="https://dgm4miccai.github.io/" target="_blank" rel="noopener">DGM4</a> workshop to talk about <a href="https://arxiv.org/abs/2308.08830" target="_blank" rel="noopener">Neural Implicit Representations for Abdominal MR Reconstruction</a> on October 8, at 10:25.</p> +</li> +<li> +<p>Hannah Eichhorn presents her work on physics-aware motion simulation for T2*-weighted MRI at the <a href="https://2023.sashimi-workshop.org/program/" target="_blank" rel="noopener">SASHIMI</a> workshop on October 8, at 14:40. Check out the <a href="https://arxiv.org/abs/2303.10987" target="_blank" rel="noopener">preprint</a> for more information!</p> +</li> +<li> +<p>Maxime Di Folco presents at the <a href="https://stacom.github.io/stacom2023/" target="_blank" rel="noopener">STACOM</a> workshop on October 12, at 11:15 the work of Josh Stein on &ldquo;Sparse annotation strategies for segmentation of short axis cardiac MRI&rdquo; (<a href="https://arxiv.org/abs/2307.12619" target="_blank" rel="noopener">preprint</a>).</p> +</li> +<li> +<p>Cosmin Bercea will talk about <a href="https://arxiv.org/pdf/2308.13861.pdf" target="_blank" rel="noopener">Bias in Unsupervised Anomaly Detection</a> at the <a href="https://faimi-workshop.github.io/2023-miccai/" target="_blank" rel="noopener">FAIMI</a> workshop on October 12, at 2:50 PDT.</p> +</li> +<li> +<p>Daniel Lang will talk about <a href="https://arxiv.org/abs/2303.05861" target="_blank" rel="noopener">Anomaly Detection in Non-Contrast Enhanced Breast MRI</a> at the <a href="https://caption-workshop.github.io/miccai2023/#Workshop%20sessions" target="_blank" rel="noopener">CaPTion</a> workshop on October 12.</p> +</li> +</ul> + + + + Abstracts accepted at 2023 ISMRM & ISMRT Annual Meeting + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + Tue, 25 Apr 2023 00:00:00 +0000 + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + <p>Veronika Spieker&rsquo;s and Hannah Eichhorn&rsquo;s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM &amp; ISMRT Annual Meeting.</p> +<p>Veronika Spieker will present her work on &ldquo;<em>Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction</em>&rdquo; on Monday, 05 June 2023 at 1:45 pm EDT.</p> +<p>Hannah Eichhorn will present her work on &ldquo;<em>Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations</em>&rdquo; on Tuesday, 06 June 2023 at 4:45 pm EDT. Check <a href="https://github.com/HannahEichhorn/T2starRealisticMotionSimulation" target="_blank" rel="noopener">this GitHub repository</a> for more information.</p> + + + + + diff --git a/author/veronika-zimmer/avatar.jpg b/author/veronika-zimmer/avatar.jpg new file mode 100644 index 0000000..f871204 Binary files /dev/null and b/author/veronika-zimmer/avatar.jpg differ diff --git a/author/veronika-zimmer/avatar_hu7d3339efd454f84887db003b20ae29f7_22328_270x270_fill_q75_lanczos_center.jpg b/author/veronika-zimmer/avatar_hu7d3339efd454f84887db003b20ae29f7_22328_270x270_fill_q75_lanczos_center.jpg new file mode 100644 index 0000000..acc1ec5 Binary files /dev/null and b/author/veronika-zimmer/avatar_hu7d3339efd454f84887db003b20ae29f7_22328_270x270_fill_q75_lanczos_center.jpg differ diff --git a/author/veronika-zimmer/index.html b/author/veronika-zimmer/index.html new file mode 100644 index 0000000..4935db7 --- /dev/null +++ b/author/veronika-zimmer/index.html @@ -0,0 +1,1179 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Veronika Zimmer | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Veronika Zimmer

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Principal Investigator

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Veronika A. Zimmer is a principal investigator at the Institute of Computer Sciences at TUM and a visiting researcher at the School of Biomedical Engineering & Imaging Sciences at King’s College London. She received her PhD in Information and Communication Technologies from the Universitat Pompeu Fabra, Barcelona, Spain, in 2017. Her research focuses on image analysis and machine learning with a particular interest in robust and generalizable methods for multimodal registration and segmentation in medical imaging.

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Interests
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Education
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    Medical Image Computing (Ph. D.), 2017

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    Universitat Pompeu Fabra, Barcelona, Spain

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    Computational Life Science (M. Sc.), 2011

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    University of Luebeck, Germany

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    Computational Life Science (B. Sc.), 2008

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    University of Luebeck, Germany

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Vicky Goh

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Technical University Munich

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Faculty of Informatics and Institute for Advanced Study

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Helmholtz Center Munich

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Institute of Machine Learning in Biomedical Imaging

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Example Event

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+ + +
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+ + Image credit: Unsplash +
+
+ + + +
+ + +

Abstract

+

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellusac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam.

+ + +
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Date
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+ Jun 1, 2030 1:00 PM — 3:00 PM +
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+ + +
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Event
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+ + Wowchemy Conference + +
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Location
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Wowchemy HQ
+ +
+
450 Serra Mall, Stanford, CA 94305
+ +
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+ + +
+ +
+

Slides can be added in a few ways:

+
    +
  • Create slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file
    • +
  • Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file
    • +
  • Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes.
    • +
+

Further event details, including page elements such as image galleries, can be added to the body of this page.

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
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Recent & Upcoming Events

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2030

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+ Example Event +
+ + + +
+ An example event. +
+ + + +
+ + +
+ + Jun 1, 2030 1:00 PM — 3:00 PM + + + + Wowchemy HQ + +
+ + + +
+ + + +
+
+ + + + + Example Event + + +
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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/event/index.xml b/event/index.xml new file mode 100644 index 0000000..fe84510 --- /dev/null +++ b/event/index.xml @@ -0,0 +1,31 @@ + + + + Recent & Upcoming Events | Computational Imaging and AI in Medicine + https://compai-lab.io/event/ + + Recent & Upcoming Events + Wowchemy (https://wowchemy.com)en-usSat, 01 Jun 2030 13:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + Recent & Upcoming Events + https://compai-lab.io/event/ + + + + Example Event + https://compai-lab.io/event/example/ + Sat, 01 Jun 2030 13:00:00 +0000 + https://compai-lab.io/event/example/ + <p>Slides can be added in a few ways:</p> +<ul> +<li><strong>Create</strong> slides using Wowchemy&rsquo;s <a href="https://wowchemy.com/docs/managing-content/#create-slides" target="_blank" rel="noopener"><em>Slides</em></a> feature and link using <code>slides</code> parameter in the front matter of the talk file</li> +<li><strong>Upload</strong> an existing slide deck to <code>static/</code> and link using <code>url_slides</code> parameter in the front matter of the talk file</li> +<li><strong>Embed</strong> your slides (e.g. Google Slides) or presentation video on this page using <a href="https://wowchemy.com/docs/writing-markdown-latex/" target="_blank" rel="noopener">shortcodes</a>.</li> +</ul> +<p>Further event details, including page elements such as image galleries, can be added to the body of this page.</p> + + + + + diff --git a/files/.DS_Store b/files/.DS_Store new file mode 100644 index 0000000..0d04c1e Binary files /dev/null and b/files/.DS_Store differ diff --git a/files/MSc_Segmentation.pdf b/files/MSc_Segmentation.pdf new file mode 100644 index 0000000..6103e91 Binary files 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deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field.}, + author = {Schnabel, Julia A. and Heinrich, Mattias P. and Papież, Bartłomiej W. and Brady, Sir J. Michael}, + doi = {10.1016/j.media.2016.06.031}, + issn = {1361-8415}, + journal = {Medical Image Analysis}, + keywords = {Demons, Discrete optimization, Registration uncertainty, Sliding motion, Supervoxels, Multi-modality}, + language = {English}, + month = {October}, + pages = {145--148}, + publisher = {Elsevier}, + title = {Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling}, + volume = {33}, + year = {2016} +} + diff --git a/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/index.html b/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/index.html new file mode 100644 index 0000000..cd0a358 --- /dev/null +++ b/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/index.html @@ -0,0 +1,1340 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling

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+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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+

Model-Based and Data-Driven Strategies in Medical Image Computing

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+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
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A topological loss function for deep-learning based image segmentation using persistent homology

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+ + + Veronika Zimmer + + +
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Veronika Zimmer
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Principal Investigator
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My research focuses on image analysis and machine learning with a particular interest in robust and generalizable methods for multimodal registration and segmentation in medical imaging.

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+ + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

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+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.}, + author = {Ilkay Oksuz and Bram Ruijsink and Esther Puyol-Antón and James R. Clough and Gastao Cruz and Aurelien Bustin and Claudia Prieto and Rene Botnar and Daniel Rueckert and schnabel and Andrew P. King}, + doi = {https://doi.org/10.1016/j.media.2019.04.009}, + issn = {1361-8415}, + journal = {Medical Image Analysis}, + keywords = {Cardiac MR motion artefacts, Image quality assessment, Artifact, Convolutional neural networks, LSTM}, + pages = {136-147}, + title = {Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning}, + url = {https://www.sciencedirect.com/science/article/pii/S1361841518306765}, + volume = {55}, + year = {2019} +} + diff --git a/fpublications/oksuz-2019136/index.html b/fpublications/oksuz-2019136/index.html new file mode 100644 index 0000000..24b30f4 --- /dev/null +++ b/fpublications/oksuz-2019136/index.html @@ -0,0 +1,1393 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

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+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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+

Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation

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+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

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+ + + + + + + + + + + + + + + + Jan 1, 2003 + + + + + + + + + + + + + + + +
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+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations

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+ + + + + + + + + + + + + + + + Jan 1, 2001 + + + + + + + + + + + + + + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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About us

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+

The Institute for Computational Imaging and AI in Medicine (CompAI) at TUM and the Institute of Machine Learning in Biomedical Imaging (IML) at Helmholtz Center Munich focus on research to leverage machine learning for the grand challenges in biomedical imaging in areas of unmet clinical need. Novel and affordable solutions should empower clinics to make more accurate, fast and reliable decisions for early detection, treatment planning and improved patient outcome. We are looking for team members, please contact us.

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+ + + + + + + + + + + + + + + + + + + + + + + + + + About us + + + +
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Meet the Team

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+ + + + + + + + +
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Chair

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+ + + + + + + + Avatar + + +
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Julia A. Schnabel

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Technical University Munich

Helmholtz Center Munich

King's College London

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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging

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Biomedical Imaging, Artificial Intelligence in Medicine

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Team Support

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Sandra Mayer

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Helmholtz Center Munich

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Administrative Assistant

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Project Management and Administration, Team Management and Support, Communication and Relations

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Sabine Franke

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Technical University Munich

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Administrative Assistant

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Project Management and Administration, Team Management and Support, Communication and Relations

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Principal Investigators

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Veronika Zimmer

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Technical University Munich

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Principal Investigator

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Medical Image Computing, Ultrasound Image Analysis, Fetal Image Analysis

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Georgios Kaissis

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Helmholtz Center Munich

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Principal Investigator

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Reliable artificial intelligence, Medical image computing, Probabilistic methods

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Senior Researchers

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Daniel M. Lang

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Helmholtz Center Munich

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Research Scientist

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Self-supervised Learning, Transfer Learning, Survival Analysis

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Emily Chan

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Helmholtz Center Munich

King's College London

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Research Scientist

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Medical image computing, Transfer learning, Multi-modal learning

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Maxime Di Folco

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Helmholtz Center Munich

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Research Scientist

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Representation learning, Cardiac imaging

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Researchers

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Anna Reithmeir

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Technical University of Munich

Munich Center for Machine Learning (MCML)

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PhD Student

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Image Registration, Physics-Inspired Regularization, Manifold-Valued Data

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Anneliese Riess

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Technical University of Munich

Helmholtz Center Munich

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PhD Student

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Mathematical Foundations of Privacy Preserving AI, Probability Theory

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Sameer Ambekar

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Technical University of Munich

Helmholtz Center Munich

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PhD Student

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Domain Generalization, Meta Learning, Variational Inference

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Hannah Eichhorn

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Helmholtz Center Munich

Technical University Munich

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PhD student

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Brain Magnetic Resonance Imaging, Image reconstruction & Motion Correction, Deep learning

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Cosmin I. Bercea

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Technical University of Munich

Helmholtz Center Munich

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PhD Student

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Generative AI, Unsupervised Representation Learning, Anomaly Detection

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Stefan Fischer

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Technical University of Munich

MRI TUM Munich

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PhD Student

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Segmentation, Radiooncology, Transfer Learning/Curriculum Learning

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Johannes Kiechle

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Technical University of Munich

MRI TUM Munich

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PhD Student

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Shape Analysis, Representation Learning, Magnetic Resonance Imaging

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Jun Li

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Technical University of Munich

Munich Center for Machine Learning (MCML)

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PhD Student

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Vision and Language, Multi-Modal Learning, Cross-Modality Generation

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Veronika Spieker

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Technical University Munich

Helmholtz Center Munich

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PhD Student

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MRI Reconstruction, Motion Detection & Correction, Neural Implicit Representations

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Alumni

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Simona Bottani

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Helmholtz Center Munich

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Research Scientist

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Deep learning, Big data sets, Brain imaging

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News

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+ Review paper accepted at IEEE Transactions on Medical Imaging +
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Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.

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+ + + + + + + + + + + + + + + + Oct 25, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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+ + + + + Review paper accepted at IEEE Transactions on Medical Imaging + + +
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+ Five papers accepted at MICCAI 2023 workshops +
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Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

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Interested to hear more about our work? Then join us at the following workshops:

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+ + + + + + + + + + + + + + + + Sep 14, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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+ + + + + Five papers accepted at MICCAI 2023 workshops + + +
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+ Two papers accepted at MICCAI 2023 +
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“What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection and Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

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We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!

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“Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening” by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/index.json b/index.json new file mode 100644 index 0000000..b9e5056 --- /dev/null +++ b/index.json @@ -0,0 +1,51 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + [{"authors":null,"categories":null,"content":"Daniel Lang will be a postdoc at the Institute of Machine Learning in Biomedical Imaging at Helmholtz Munich. His research interest focuses on the application of deep learning models for problem settings in the field of medical imaging with a special focus on cancer management. He is particularly interested in topics like transfer and selfsupervised learning, out of distribution problems and domain adaptation, and survival analysis.\n","date":1694649600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1694649600,"objectID":"56a7ac3a8e494744517e46962a75d3a1","permalink":"https://compai-lab.io/author/daniel-m.-lang/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/daniel-m.-lang/","section":"authors","summary":"Daniel Lang will be a postdoc at the Institute of Machine Learning in Biomedical Imaging at Helmholtz Munich. His research interest focuses on the application of deep learning models for problem settings in the field of medical imaging with a special focus on cancer management.","tags":null,"title":"Daniel M. Lang","type":"authors"},{"authors":null,"categories":null,"content":"Veronika A. Zimmer is a principal investigator at the Institute of Computer Sciences at TUM and a visiting researcher at the School of Biomedical Engineering \u0026amp; Imaging Sciences at King’s College London. She received her PhD in Information and Communication Technologies from the Universitat Pompeu Fabra, Barcelona, Spain, in 2017. Her research focuses on image analysis and machine learning with a particular interest in robust and generalizable methods for multimodal registration and segmentation in medical imaging.\n","date":1668988800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1668988800,"objectID":"158e43a2a799d5339b037ca70e05c114","permalink":"https://compai-lab.io/author/veronika-zimmer/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/veronika-zimmer/","section":"authors","summary":"Veronika A. Zimmer is a principal investigator at the Institute of Computer Sciences at TUM and a visiting researcher at the School of Biomedical Engineering \u0026 Imaging Sciences at King’s College London.","tags":null,"title":"Veronika Zimmer","type":"authors"},{"authors":null,"categories":null,"content":"Anna Reithmeir is a PhD student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her B.Sc. and M.Sc. in Informatics from TU Munich with a focus on computer vision and high performance computing. In her Master’s thesis at the Munich Institute for Robotics and Machine Intelligence (MIRMI), she developed a novel algorithm for human-robot manipulability domain adaptation. Her research interests lie in the analysis and development of robust and data-driven models for image registration, numerics of machine learning algorithms and Riemannian manifolds.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"7f82a87a09ad8ec738b3f2f205bbd5d4","permalink":"https://compai-lab.io/author/anna-reithmeir/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/anna-reithmeir/","section":"authors","summary":"Anna Reithmeir is a PhD student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her B.Sc. and M.Sc. in Informatics from TU Munich with a focus on computer vision and high performance computing.","tags":null,"title":"Anna Reithmeir","type":"authors"},{"authors":null,"categories":null,"content":"Anneliese Riess is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University Munich (TUM). She received her B.Sc. and M.Sc. in Mathematics at TUM and devoted a substantial part of her studies to the field of probability theory. In her Master’s thesis she investigated Majority Voting Processes, a class of interacting particle systems. The main focus of the thesis was the equilibrium behaviour of such stochastic models. Prior to her PhD, she worked on two different projects at the university in her final year of her Master’s degree. In the first project, she worked on creating a model that describes the behaviour of DNA methylation. The second project involved modelling and analysing the propagation of underground water. Her research interests lie in the mathematical foundations of privacy-preserving artificial intelligence.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"27ee63b9158a8e603cca45e3f15c2184","permalink":"https://compai-lab.io/author/anneliese-riess/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/anneliese-riess/","section":"authors","summary":"Anneliese Riess is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University Munich (TUM). She received her B.Sc. and M.","tags":null,"title":"Anneliese Riess","type":"authors"},{"authors":null,"categories":null,"content":"Emily Chan is a postdoctoral researcher at the Institute of Machine Learning for Biomedical Imaging at Helmholtz Munich. She received her PhD in 2022 from King’s College London, where she worked on utilising classical machine learning and deep learning techniques with limited and imbalanced data for MR liver imaging, in collaboration with Perspectum. She is particularly interested in the automation of various clinically-relevant tasks in radiology, with her research at the IML focusing on deep learning for the early diagnosis and prognosis of Alzheimer’s disease.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"56687b7fcbaceb3c4ed6d5b35f5c4e2a","permalink":"https://compai-lab.io/author/emily-chan/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/emily-chan/","section":"authors","summary":"Emily Chan is a postdoctoral researcher at the Institute of Machine Learning for Biomedical Imaging at Helmholtz Munich. She received her PhD in 2022 from King’s College London, where she worked on utilising classical machine learning and deep learning techniques with limited and imbalanced data for MR liver imaging, in collaboration with Perspectum.","tags":null,"title":"Emily Chan","type":"authors"},{"authors":null,"categories":null,"content":"Georgios Kaissis is a principal investigator at the Institute of Biomedical Machine Learning (IML) at the Helmholtz Center Munich, a senior research scientist at the Institute of Artificial Intelligence and Informatics in Medicine and specialist diagnostic radiologist at the Institute for Radiology at TUM, a postdoctoral researcher at the Department of Computing at Imperial College London and leads the Healthcare Unit at OpenMined. His research concentrates on biomedical image analysis with a focus on next-generation privacy-preserving machine learning methods as well as probabilistic methods for the design and deployment of robust, secure, fair and transparent machine learning algorithms to medical imaging workflows.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"215e356043d31829796b4b4b033d3054","permalink":"https://compai-lab.io/author/georgios-kaissis/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/georgios-kaissis/","section":"authors","summary":"Georgios Kaissis is a principal investigator at the Institute of Biomedical Machine Learning (IML) at the Helmholtz Center Munich, a senior research scientist at the Institute of Artificial Intelligence and Informatics in Medicine and specialist diagnostic radiologist at the Institute for Radiology at TUM, a postdoctoral researcher at the Department of Computing at Imperial College London and leads the Healthcare Unit at OpenMined.","tags":null,"title":"Georgios Kaissis","type":"authors"},{"authors":null,"categories":null,"content":"Sameer Ambekar is a Ph.D. Student at the Technical University of Munich (TUM). He received his Masters in Artificial Intelligence (MSc AI) from the University of Amsterdam (UvA), Netherlands. For his Master’s thesis (48 ECTS), he worked on ‘Test-Time Adaptation for Domain Generalization by generating models and labels through Variational meta-learning’ at the AIM Lab, UvA. Prior to his master’s, he worked as a Research Assistant (RA) at IIT Delhi (IITD) to address Unsupervised Domain Adaptation through methods such as Variational generative latent search. He is interested in solving problems in unsupervised learning through methods such as meta-learning and variational inference alongside learning efficient and transferable features.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"1da190d086e25ec10dadfa3caf051b57","permalink":"https://compai-lab.io/author/sameer-ambekar/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/sameer-ambekar/","section":"authors","summary":"Sameer Ambekar is a Ph.D. Student at the Technical University of Munich (TUM). He received his Masters in Artificial Intelligence (MSc AI) from the University of Amsterdam (UvA), Netherlands. For his Master’s thesis (48 ECTS), he worked on ‘Test-Time Adaptation for Domain Generalization by generating models and labels through Variational meta-learning’ at the AIM Lab, UvA.","tags":null,"title":"Sameer Ambekar","type":"authors"},{"authors":null,"categories":null,"content":"Sandra Mayer supports the Lab for Computational Imaging and AI in Medicine as a member of the administrative staff at the Helmholtz Campus in Neuherberg.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"c7e1fe3fbec405988b58cc78bff18671","permalink":"https://compai-lab.io/author/sandra-mayer/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/sandra-mayer/","section":"authors","summary":"Sandra Mayer supports the Lab for Computational Imaging and AI in Medicine as a member of the administrative staff at the Helmholtz Campus in Neuherberg.","tags":null,"title":"Sandra Mayer","type":"authors"},{"authors":null,"categories":null,"content":"Simona Bottani is a PostDoctoral Fellow at the IML where she works on deep learning applied to big research medical imaging cohort. She received her PhD in computer science from Sorbonne University in April 2022. She worked at the ARAMIS Lab and her thesis focused on the application of deep learning models for neuroimaging studies using a large scale clinical data warehouse of the Paris Great Area Hospitals (AP-HP). From 2017 to 2018 she worked as research engineer in the ARAMIS Lab. She received a Master Degree in 2016 and a Bachelor degree in 2014 in Biomedical engineering from Politecnico di Torino.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"e574d46ca998273d26d337b8256da4a3","permalink":"https://compai-lab.io/author/simona-bottani/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/simona-bottani/","section":"authors","summary":"Simona Bottani is a PostDoctoral Fellow at the IML where she works on deep learning applied to big research medical imaging cohort. She received her PhD in computer science from Sorbonne University in April 2022.","tags":null,"title":"Simona Bottani","type":"authors"},{"authors":null,"categories":null,"content":"Hannah Eichhorn is a PhD student at the Institute of Machine Learning in Biomedical Imaging (IML), Helmholtz Munich. She received her B.Sc. in Physics from Heidelberg University and her M.Sc. in Bio- and Medical Physics from University of Copenhagen. In her Master’s thesis at the Neurobiology Research Unit, Copenhagen University Hospital, she worked on prospective motion correction for brain magnetic resonance imaging (MRI). Her doctoral research focuses on deep-learning based reconstruction and motion correction of multi-parametric brain MRI, in collaboration with the Neuroscientific MR-Physics research group at Klinikum rechts der Isar (TUM).\n","date":1698192000,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1698192000,"objectID":"53fd79ba9ff7f449cf98e3e77a65136a","permalink":"https://compai-lab.io/author/hannah-eichhorn/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/hannah-eichhorn/","section":"authors","summary":"Hannah Eichhorn is a PhD student at the Institute of Machine Learning in Biomedical Imaging (IML), Helmholtz Munich. She received her B.Sc. in Physics from Heidelberg University and her M.Sc. in Bio- and Medical Physics from University of Copenhagen.","tags":null,"title":"Hannah Eichhorn","type":"authors"},{"authors":null,"categories":null,"content":"Cosmin Bercea is a Ph.D. Student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University of Munich (TUM). He received his B.Sc. and M.Sc. in Computer Science from the FAU University in Erlangen, Germany, focusing on pattern recognition and medical image analysis. In his Master’s thesis at Siemens Healthineers in Erlangen, he built novel shared memory neural networks for medical imaging. Before his Ph.D., he worked as a research engineer at Bosch Corporate Research, developing deep learning algorithms for scene understanding for self-driving cars. His research interests lie in interpretable machine learning algorithms for anomaly detection.\n","date":1694649600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1694649600,"objectID":"1a5f197a0ae6843b5eca188c8e7eddb7","permalink":"https://compai-lab.io/author/cosmin-i.-bercea/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/cosmin-i.-bercea/","section":"authors","summary":"Cosmin Bercea is a Ph.D. Student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University of Munich (TUM). He received his B.Sc. and M.","tags":null,"title":"Cosmin I. Bercea","type":"authors"},{"authors":null,"categories":null,"content":"Maxime Di Folco is a PostDoctoral researcher at the Institute of Machine Learning for Biomedical Imaging at Helmholtz Center Munich. His research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods, that aim to acquire low dimensional representation of high dimensional data, with a strong focus on cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.\n","date":1694649600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1694649600,"objectID":"f7695d783c3739ededca3e573e80f73a","permalink":"https://compai-lab.io/author/maxime-di-folco/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/maxime-di-folco/","section":"authors","summary":"Maxime Di Folco is a PostDoctoral researcher at the Institute of Machine Learning for Biomedical Imaging at Helmholtz Center Munich. His research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods, that aim to acquire low dimensional representation of high dimensional data, with a strong focus on cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.","tags":null,"title":"Maxime Di Folco","type":"authors"},{"authors":null,"categories":null,"content":"Sabine Franke supports the Lab for Computational Imaging and AI in Medicine as a member of the administrative staff at the TU campus in Garching. She graduated in 2014 from the University of Graz, Austria, with a degree in conference interpreting for German, English and Spanish. Before joining the team at the TU Munich, she spent several years working as a translator and interpreter in Germany as well as in the Netherlands, adding Dutch to her working languages.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"4a830eefc644326eef396fe2fbc34028","permalink":"https://compai-lab.io/author/sabine-franke/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/sabine-franke/","section":"authors","summary":"Sabine Franke supports the Lab for Computational Imaging and AI in Medicine as a member of the administrative staff at the TU campus in Garching. She graduated in 2014 from the University of Graz, Austria, with a degree in conference interpreting for German, English and Spanish.","tags":null,"title":"Sabine Franke","type":"authors"},{"authors":null,"categories":null,"content":"Stefan Fischer is a Ph.D. Student at the Technical University of Munich (TUM). He received his B.Sc. and M.Sc. from FAU in Erlangen, Germany with a focus on medical image analysis. In his Master’s thesis at the Radiooncology department of the university hospital Erlangen, he build a generative approach for brain metastasis for data augmentation in MR Imaging. His research interest lies in deep learning based segmentation, transfer learning and curriculum learning.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"0dd4daba56f8a163ca9cb4738bf3f8cf","permalink":"https://compai-lab.io/author/stefan-fischer/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/stefan-fischer/","section":"authors","summary":"Stefan Fischer is a Ph.D. Student at the Technical University of Munich (TUM). He received his B.Sc. and M.Sc. from FAU in Erlangen, Germany with a focus on medical image analysis.","tags":null,"title":"Stefan Fischer","type":"authors"},{"authors":null,"categories":null,"content":"Johannes Kiechle is a Ph.D. Student at the Technical University of Munich. He received his B.Eng. from Munich University of Applied Sciences and M.Sc. from Technical University of Munich. In his Master’s thesis, he investigated the shape change of the human hippocampus in the course of ageing within a population of healthy individuals using graph neural networks. For his PhD project, he works in collaboration with the department of Radiation Oncology at the University Hospital rechts der Isar. Therein the focus is on the development and validation of histology-specific AI-based decision support systems for soft-tissue-sarcoma patients.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"e6b4d7d43284e202528372a69251e3af","permalink":"https://compai-lab.io/author/johannes-kiechle/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/johannes-kiechle/","section":"authors","summary":"Johannes Kiechle is a Ph.D. Student at the Technical University of Munich. He received his B.Eng. from Munich University of Applied Sciences and M.Sc. from Technical University of Munich. In his Master’s thesis, he investigated the shape change of the human hippocampus in the course of ageing within a population of healthy individuals using graph neural networks.","tags":null,"title":"Johannes Kiechle","type":"authors"},{"authors":null,"categories":null,"content":"Jun Li is a Ph.D. Student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her M.E. in Computer Technology from the University of Chinese Academy of Sciences, China. In her Master’s thesis, she developed a novel framework that combines supervised and unsupervised learning for ultrasound report generation. Her research interests lie in Vision and Language, Multi-Modal Learning, and Cross-Modality Generation.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"a3945b0cc375cd0742d99649c0c5f929","permalink":"https://compai-lab.io/author/jun-li/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/jun-li/","section":"authors","summary":"Jun Li is a Ph.D. Student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her M.E. in Computer Technology from the University of Chinese Academy of Sciences, China.","tags":null,"title":"Jun Li","type":"authors"},{"authors":null,"categories":null,"content":"Veronika Spieker is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Munich and Technical University of Munich (TUM). After completing her B.Sc. in engineering at TU Darmstadt and Virginia Tech, she pursued her interest in the medical domain with a M.Sc. in Medical Technologies at TUM. For her PhD project, she works on Physics-Based AI for Motion Correction in Abdominal MRI in collaboration with the Body Magnetic Resonance Group at the Klinikum rechts der Isar. Her research interests include concepts such as neural implicit representations and it’s application to MR reconstruction and motion estimation.\n","date":1698192000,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1698192000,"objectID":"07e3d72feca02657047b62f64818eee0","permalink":"https://compai-lab.io/author/veronika-spieker/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/veronika-spieker/","section":"authors","summary":"Veronika Spieker is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Munich and Technical University of Munich (TUM). After completing her B.Sc. in engineering at TU Darmstadt and Virginia Tech, she pursued her interest in the medical domain with a M.","tags":null,"title":"Veronika Spieker","type":"authors"},{"authors":null,"categories":null,"content":"Julia A. Schnabel is Professor of Computational Imaging and AI in Medicine at Technical University of Munich (TUM Liesel Beckmann Distinguished Professorship) and Director of a new Institute of Machine Learning in Biomedical Imaging at Helmholtz Center Munich (Helmholtz Distinguished Professorship), with secondary appointment as Chair in Computational Imaging at King’s College London. She graduated in Computer Science (equiv. MSc) from Technical University of Berlin, Berlin, Germany, and was awarded the PhD in Computer Science from University College London, UK. In 2007, she joined the University of Oxford, UK as Associate Professor in Engineering Science (Medical Imaging), where she became Full Professor of Engineering Science by Recognition of Distinction in 2014. She joined King’s College London as a new Chair in 2015, and in 2021 joined TUM and Helmholtz Munich for her current positions. Her research interests include machine/deep learning, nonlinear motion modeling, as well as multimodality and quantitative imaging, for cancer imaging, cardiac imaging, neuroimaging and perinatal imaging. Dr. Schnabel has been elected Fellow of IEEE (2021), Fellow of ELLIS (2019), and Fellow of the MICCAI Society (2018). She is an Associate Editor of the IEEE Transactions on Medical Imaging on whose steering board she serves since 2021, the IEEE Transactions of Biomedical Engineering, on the Editorial Board of Medical Image Analysis and Executive/Founding Editor of MELBA. She currently serves as elected Technical Representative on IEEE EMBS AdCom, as voting member of the IEEE EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP), as Executive Secretary to the MICCAI board, and as member of ELLIS Health and ELLIS Munich.\n","date":1697155200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1697155200,"objectID":"1e0f1c9788b3f556def3696f7482620c","permalink":"https://compai-lab.io/author/julia-a.-schnabel/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/julia-a.-schnabel/","section":"authors","summary":"Julia A. Schnabel is Professor of Computational Imaging and AI in Medicine at Technical University of Munich (TUM Liesel Beckmann Distinguished Professorship) and Director of a new Institute of Machine Learning in Biomedical Imaging at Helmholtz Center Munich (Helmholtz Distinguished Professorship), with secondary appointment as Chair in Computational Imaging at King’s College London.","tags":null,"title":"Julia A. Schnabel","type":"authors"},{"authors":[],"categories":null,"content":"Slides can be added in a few ways:\n Create slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://compai-lab.io/event/example/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/event/example/","section":"event","summary":"An example event.","tags":[],"title":"Example Event","type":"event"},{"authors":["Veronika Spieker","Hannah Eichhorn"],"categories":null,"content":"Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.\nMotion remains a major challenge in MRI and various deep learning solutions have been proposed – but what are common challenges and potentials? Check out this review, which identifies differences and synergies of recent methods and bridges the gap between AI and MR physics.\n","date":1698192000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1698192000,"objectID":"1a2d9a9d1a4a2f793c01f725af470a0f","permalink":"https://compai-lab.io/post/spieker_eichhorn_tmi/","publishdate":"2023-10-25T00:00:00Z","relpermalink":"/post/spieker_eichhorn_tmi/","section":"post","summary":"Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.\n","tags":null,"title":"Review paper accepted at IEEE Transactions on Medical Imaging","type":"post"},{"authors":["Veronika Spieker","Hannah Eichhorn","Kerstin Hammernik","Daniel Rueckert","Christine Preibisch","Dimitrios C. Karampinos","Julia A. Schnabel"],"categories":null,"content":"","date":1697155200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1697155200,"objectID":"418f5e56e8368b75a4c8427055496058","permalink":"https://compai-lab.io/publication/spiekereichhorn-2023-review/","publishdate":"2023-10-13T00:00:00Z","relpermalink":"/publication/spiekereichhorn-2023-review/","section":"publication","summary":"Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.","tags":["motion correction","motion compensation","magnetic resonance imaging","deep learning"],"title":"Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review","type":"publication"},{"authors":["Hannah Eichhorn","Veronika Spieker","Cosmin I. Bercea","Daniel M. Lang","Maxime Di Folco"],"categories":null,"content":"Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.\nInterested to hear more about our work? Then join us at the following workshops:\n Veronika Spieker will be at the DGM4 workshop to talk about Neural Implicit Representations for Abdominal MR Reconstruction on October 8, at 10:25.\n Hannah Eichhorn presents her work on physics-aware motion simulation for T2*-weighted MRI at the SASHIMI workshop on October 8, at 14:40. Check out the preprint for more information!\n Maxime Di Folco presents at the STACOM workshop on October 12, at 11:15 the work of Josh Stein on “Sparse annotation strategies for segmentation of short axis cardiac MRI” (preprint).\n Cosmin Bercea will talk about Bias in Unsupervised Anomaly Detection at the FAIMI workshop on October 12, at 2:50 PDT.\n Daniel Lang will talk about Anomaly Detection in Non-Contrast Enhanced Breast MRI at the CaPTion workshop on October 12.\n ","date":1694649600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1694649600,"objectID":"884cf9b1cab38ee1cc29cecd68271fe5","permalink":"https://compai-lab.io/post/iml_miccai_workshops/","publishdate":"2023-09-14T00:00:00Z","relpermalink":"/post/iml_miccai_workshops/","section":"post","summary":"Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.\nInterested to hear more about our work? Then join us at the following workshops:\n","tags":null,"title":"Five papers accepted at MICCAI 2023 workshops","type":"post"},{"authors":null,"categories":null,"content":"Course details\nConsidering the manifold of medical imaging data, i.e. the underlying topological space, facilitates the analysis, interpretation, and visualization of the data. This seminar focuses on machine and deep learning methods that either learn the manifold from high-dimensional data or use manifold-valued data as input. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed. This includes, but is not limited to:\n Introduction to manifolds Difference between learning on and of a manifold Examples of manifold-valued data in medical imaging State-of-the-art methods for manifold-valued data Clinical applications Please register to: https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx\nCheck the intro slides here: ","date":1689724800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689724800,"objectID":"608933ad4ec501fd658046c4f7f567c3","permalink":"https://compai-lab.io/teaching/manifold_seminar/","publishdate":"2023-07-19T00:00:00Z","relpermalink":"/teaching/manifold_seminar/","section":"teaching","summary":"Winter semester 2023. TUM Informatics. Master Seminar.","tags":["winter"],"title":"Learning of and on manifolds in medical imaging (IN2107)","type":"teaching"},{"authors":null,"categories":null,"content":" Anomaly detection aims to identify patterns that do not conform to the expected normal distribution. Despite its importance for clinical applications, the detection of outliers is still a very challenging task due to the rarity, unknownness, diversity, and heterogeneity of anomalies. Basic problem formulations to recent advances in the field will be discussed.\nThis includes, but is not limited to:\n Reconstruction-based anomaly segmentation Probabilistic models, i.e., anomaly likelihood estimation Generative models Self-supervised-, contrastive methods Unsupervised methods Clinical Applications Please register via the TUM matching system: https://matching.in.tum.de\nCheck the intro slides here: ","date":1689724800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1689724800,"objectID":"ab09053a2a206bcd7410b9140648bd24","permalink":"https://compai-lab.io/teaching/anomaly_seminar/","publishdate":"2023-07-19T00:00:00Z","relpermalink":"/teaching/anomaly_seminar/","section":"teaching","summary":"Winter semester 2023. TUM Informatics. Master Seminar.","tags":["winter"],"title":"Unsupervised Anomaly Detection in Medical Imaging","type":"teaching"},{"authors":["Maxime Di Folco"],"categories":null,"content":"Abstract:\nThis Master’s project aims to explore the use of covariance descriptors for disease classification with medical images. First, the MedMNIST toy dataset will be explored. Then, the student will work with an open-source medical dataset, e.g. of 2D chest x-ray or 3D cardiac MR images\n","date":1688774400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1688774400,"objectID":"fbf30eeb3802ff19335a74de2c53379c","permalink":"https://compai-lab.io/vacancies/msc_manifold_anna/","publishdate":"2023-07-08T00:00:00Z","relpermalink":"/vacancies/msc_manifold_anna/","section":"vacancies","summary":"Master Thesis. [I'm interested](mailto:anna.reithmeir@tum.de)","tags":["master"],"title":"Exploring Riemannian Manifolds for Medical Image Classification (f/m/x)","type":"vacancies"},{"authors":["Cosmin I. Bercea"],"categories":null,"content":"“What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection and Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.\n Curios what auto-encoders actually learn? Check out this project page to find out more. How can we reverse anomalies in medical images? Check out the project here. ","date":1685059200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1685059200,"objectID":"a0049566b02e8586cbfe27077c921162","permalink":"https://compai-lab.io/post/bercea_miccai/","publishdate":"2023-05-26T00:00:00Z","relpermalink":"/post/bercea_miccai/","section":"post","summary":"“What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection and Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.\n","tags":null,"title":"Two papers accepted at MICCAI 2023","type":"post"},{"authors":["Cosmin I. Bercea"],"categories":null,"content":"We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!\nCurious about how to solve the noise paradox illustrated below? Check out our project page.\n ","date":1684972800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1684972800,"objectID":"34b7cbc76b0e5cbc25a6459ba0d80ec5","permalink":"https://compai-lab.io/post/bercea_icml/","publishdate":"2023-05-25T00:00:00Z","relpermalink":"/post/bercea_icml/","section":"post","summary":"We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!\n","tags":null,"title":" Paper accepted at ICML IMLH 2023","type":"post"},{"authors":["Cosmin I. Bercea"],"categories":null,"content":"“Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening” by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.\n Moving beyond hyperintensity thresholding: This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection. Our proposed method RA outperformed SOTA methods on T1w brain MRIs, detecting more global anomalies (AUROC increased from 73.1 to 89.4) and local pathologies (detection rate increased from 52.6% to 86.0%).\nWant to know more? Check the project site.\n","date":1682640000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1682640000,"objectID":"3666dc044c1666623a60e6d9f049d1c6","permalink":"https://compai-lab.io/post/bercea_midl/","publishdate":"2023-04-28T00:00:00Z","relpermalink":"/post/bercea_midl/","section":"post","summary":"“Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening” by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.\n","tags":null,"title":"Paper accepted at MIDL 2023 (oral talk)","type":"post"},{"authors":["Hannah Eichhorn","Veronika Spieker"],"categories":null,"content":"Veronika Spieker’s and Hannah Eichhorn’s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM \u0026amp; ISMRT Annual Meeting.\nVeronika Spieker will present her work on “Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction” on Monday, 05 June 2023 at 1:45 pm EDT.\nHannah Eichhorn will present her work on “Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations” on Tuesday, 06 June 2023 at 4:45 pm EDT. Check this GitHub repository for more information.\n","date":1682380800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1682380800,"objectID":"6779a3ca2f781bb0731d6a43569954cf","permalink":"https://compai-lab.io/post/spieker_eichhorn_ismrm/","publishdate":"2023-04-25T00:00:00Z","relpermalink":"/post/spieker_eichhorn_ismrm/","section":"post","summary":"Veronika Spieker’s and Hannah Eichhorn’s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM \u0026 ISMRT Annual Meeting.\nVeronika Spieker will present her work on “Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction” on Monday, 05 June 2023 at 1:45 pm EDT.","tags":null,"title":"Abstracts accepted at 2023 ISMRM \u0026 ISMRT Annual Meeting","type":"post"},{"authors":["Veronika Zimmer"],"categories":null,"content":"Abstract:\nIn recent years, unsupervised and semi-supervised learning from populations of surfaces and curves has received a lot of attention. Such data representations are analyzed according to their shapes which open a broad range of applications in machine learning, robotics, statistics and engineering. In particular, studying the shape of surfaces have become an important tool in biology and medical imaging. The extraction of appropriate data representations, such as triangulated surfaces, is crucial for the subsequent analysis. These surfaces are for example obtained from binary segmentations or 3D point clouds. Using standard methods, such surfaces are often not very accurate and require several post-processing steps, such as smoothing and simplifications. Deep learning based methods are of great interest in various fields such as medical imaging, com- puter vision, applied mathematics and are successfully used in the field of image segmentation. Gener- ally, a specific formulation requires a particular attention to representations, loss functions, probability models, optimization techniques, etc. This choice is very crucial due to the underlying geometry on the space of representations and constraints. we aim to develop a new set of automatic methods that can compute a triangulation and a normal field from a 3D dataset (binary image and/or 3D point cloud). The goal of this project is to understand the-state-of-the-art methods (e.g., [?]) and to propose solutions in the context of constructing a mesh from 3D images/point sets. We are interested in learn- ing from a dataset of smooth surfaces and their corresponding 3D datasets to make the triangulation or resampling accurate. The application will be the extraction of a smooth surfaces from μ-CT and CT data of the cochlea and inner ear, whose shapes can then be analyzed subsequently for population studies. To summarize, the key steps are : (i) Literature review and getting familiar with some state-of- the-art methods in the medical context; (ii) Implementing and testing the code before validation on real data; (iii) Optimizing the code and comparing with baseline methods. If successful, the method would be applied to analyze and classify surfaces.\n","date":1668988800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1668988800,"objectID":"bf9da21574765eff2ab550ec54c26376","permalink":"https://compai-lab.io/vacancies/msc_surface/","publishdate":"2022-11-21T00:00:00Z","relpermalink":"/vacancies/msc_surface/","section":"vacancies","summary":"Master Thesis. [I'm interested](mailto:veronika.zimmer@tum.de?Subject=Master%20Thesis%20IML%20(Zimmer))","tags":["master"],"title":"Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x)","type":"vacancies"},{"authors":["Cosmin I. Bercea"],"categories":null,"content":"Federated disentangled representation learning for unsupervised brain anomaly detection by Cosmin I. Bercea et al. has been published at Nature Machine Intelligence.\n In this work, a federated algorithm was trained on more than 1,500 MR scans of healthy study participants from four institutions while maintaining data privacy with the goal to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before.\nCheck the project site for more information.\n","date":1659398400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1659398400,"objectID":"8ea70f12485b23c24b24494b5219e7ae","permalink":"https://compai-lab.io/post/bercea_nature/","publishdate":"2022-08-02T00:00:00Z","relpermalink":"/post/bercea_nature/","section":"post","summary":"Federated disentangled representation learning for unsupervised brain anomaly detection by Cosmin I. Bercea et al. has been published at Nature Machine Intelligence.\n","tags":null,"title":"New publication at Nature Machine Intelligence","type":"post"},{"authors":["Cosmin I. Bercea","Daniel Rueckert","Julia A. Schnabel"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. ","date":1654646400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1654646400,"objectID":"b7524a66848d63f0c0b893828fe0f4e4","permalink":"https://compai-lab.io/publication/bercea-2022-we/","publishdate":"2022-06-08T00:00:00Z","relpermalink":"/publication/bercea-2022-we/","section":"publication","summary":"Even though auto-encoders (AEs) have the desirable property of learning compact representations without labels and have been widely applied to out-of-distribution (OoD) detection, they are generally still poorly understood and are used incorrectly in detecting outliers where the normal and abnormal distributions are strongly overlapping. In general, the learned manifold is assumed to contain key information that is only important for describing samples within the training distribution, and that the reconstruction of outliers leads to high residual errors. However, recent work suggests that AEs are likely to be even better at reconstructing some types of OoD samples. In this work, we challenge this assumption and investigate what auto-encoders actually learn when they are posed to solve two different tasks. First, we propose two metrics based on the Fréchet inception distance (FID) and confidence scores of a trained classifier to assess whether AEs can learn the training distribution and reliably recognize samples from other domains. Second, we investigate whether AEs are able to synthesize normal images from samples with abnormal regions, on a more challenging lung pathology detection task. We have found that state-of-the-art (SOTA) AEs are either unable to constrain the latent manifold and allow reconstruction of abnormal patterns, or they are failing to accurately restore the inputs from their latent distribution, resulting in blurred or misaligned reconstructions. We propose novel deformable auto-encoders (MorphAEus) to learn perceptually aware global image priors and locally adapt their morphometry based on estimated dense deformation fields. We demonstrate superior performance over unsupervised methods in detecting OoD and pathology.","tags":["unsupervised outlier detection"],"title":"What do we learn? Debunking the Myth of Unsupervised Outlier Detection","type":"publication"},{"authors":null,"categories":null,"content":"The MedtecLIVE Talent Award 2022 is given to bachelor’s and master’s theses that relate to an innovation, improvement, or new application in medical technology along with its entire value chain.\nAfter a first screening of her thesis abstract, Veronika was invited to the live finale in Stuttgart to present her thesis in an 8-minute pitch. The extensiveness of her work, her drive to clinical translation as well as visual and interactive presentation convinced the jury to award her the first prize.\nAs part of her M.Sc. in Medical Technologies at TUM, Veronika conducted her master thesis at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and published her results in Sensors (www.mdpi.com/1424-8220/21/21/7404).\nWe are happy, that she is now pursuing her PhD at our lab at Helmholtz Munich!\nMore information on the finale can be found here:\n https://medizin-und-technik.industrie.de/medizintechnik-studium/talent-award-zur-medtec-live-with-t4m-jetzt-ist-der-nachwuchs-dran/\n https://www.mirmi.tum.de/mirmi/news/article/veronika-spieker-is-honored-with-the-1st-place-medteclive-talent-award-2022/\n ","date":1653868800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1653868800,"objectID":"804d7686387465b4cb1338cbeaa88ada","permalink":"https://compai-lab.io/post/spieker_award/","publishdate":"2022-05-30T00:00:00Z","relpermalink":"/post/spieker_award/","section":"post","summary":"The MedtecLIVE Talent Award 2022 is given to bachelor’s and master’s theses that relate to an innovation, improvement, or new application in medical technology along with its entire value chain.\n","tags":null,"title":"Veronika Spieker wins the 1st place MedtecLIVE Talent Award 2022","type":"post"},{"authors":["Inês P Machado","Esther Puyol-Antón","Kerstin Hammernik","Gastao Cruz","Devran Ugurlu","Ihsane Olakorede","Ilkay Oksuz","Bram Ruijsink","Miguel Castelo-Branco","Alistair A Young"," others"],"categories":null,"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1640995200,"objectID":"dc15a7fe4ba93cce4a725c3e01ab178d","permalink":"https://compai-lab.io/publication/machado-2022-deep/","publishdate":"2022-06-24T10:30:12.832986Z","relpermalink":"/publication/machado-2022-deep/","section":"publication","summary":"","tags":null,"title":"A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis","type":"publication"},{"authors":["Lei Li","Veronika Zimmer","Julia A. Schnabel","Xiahai Zhuang"],"categories":null,"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1640995200,"objectID":"09f078aabca6169ef899b5428d19ba16","permalink":"https://compai-lab.io/publication/li-2022-atrialjsqnet/","publishdate":"2022-06-24T10:30:12.833818Z","relpermalink":"/publication/li-2022-atrialjsqnet/","section":"publication","summary":"","tags":null,"title":"AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information","type":"publication"},{"authors":["Laura Dal Toso","Zacharias Chalampalakis","Irène Buvat","Claude Comtat","Gary Cook","Vicky Goh","Julia A. Schnabel","Paul K Marsden"],"categories":null,"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1640995200,"objectID":"085ad5afaa9651a98e95664343f4dd6b","permalink":"https://compai-lab.io/publication/dal-2022-improved/","publishdate":"2022-06-24T10:30:12.833404Z","relpermalink":"/publication/dal-2022-improved/","section":"publication","summary":"","tags":null,"title":"Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning","type":"publication"},{"authors":null,"categories":null,"content":"Course details\nImage registration is the process of aligning two or more images, and crucial for many image analysis pipelines. This seminar will cover selected material of image registration for medical imaging. Basic problem formulations to recent advances in the field will be discussed. This includes, but is not limited to:\n Learning and non-learning based image registration Optimization techniques Image registration for multi-modal data Multi-resolution and regularization strategies Linear and non-linear deformations Supervised and unsupervised learning Clinical applications ","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1640995200,"objectID":"e6865756e572cd0f07494f11177e0ada","permalink":"https://compai-lab.io/teaching/master_seminar/","publishdate":"2022-01-01T00:00:00Z","relpermalink":"/teaching/master_seminar/","section":"teaching","summary":"Summer semester 2022. TUM Informatics. Master Seminar. [Details](https://campus.tum.de/tumonline/pl/ui/$ctx/wbLv.wbShowLVDetail?pStpSpNr=950627128)","tags":["summer"],"title":"Medical Image Registation I (IN2107)","type":"teaching"},{"authors":null,"categories":null,"content":"","date":1640995200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1640995200,"objectID":"3907b05cfd213ffa5292c8acbb70daad","permalink":"https://compai-lab.io/vacancies/phd-job/","publishdate":"2022-01-01T00:00:00Z","relpermalink":"/vacancies/phd-job/","section":"vacancies","summary":"PhD position starting July 2022. Position related to [MCML Munich](https://mcml.ai). [I'm interested](mailto:iml.office@helmholtz-munich.de?Subject=PhD%20Candidate%20MCML)","tags":["phd"],"title":"PhD AI-enabled medical imaging (f/m/x)","type":"vacancies"},{"authors":null,"categories":null,"content":" Course Details Basic Information At the end of the module students should be able to recall the important topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own deep learning projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.\n Introduction: Clinical motivation, clinical data, clinical workflows ML for medical imaging• Data curation for medical applications Domain shift in medical applications: Adversarial learning and Transfer learning Self-supervised learning and unsupervised learning Learning from sparse and noisy data ML for unstructured and multi-modal clinical data NLP for clinical data• Bayesian approaches to deep learning and uncertainty Interpretability and explainability Federated learning, privacy-preserving ML and ethics ML for time-to-event modeling, survival models ML for differential diagnosis and stratification• Clinical applications in pathology/radiology/omics ","date":1633046400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1633046400,"objectID":"c60a51356750103a419f30fff1615555","permalink":"https://compai-lab.io/teaching/aim_lecture/","publishdate":"2021-10-01T00:00:00Z","relpermalink":"/teaching/aim_lecture/","section":"teaching","summary":"Winter 2021. TUM Informatics. Lecture. [Details](https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950596772).","tags":["winter"],"title":"Artificial Intelligence in Medicine (IN2403)","type":"teaching"},{"authors":null,"categories":null,"content":" Course Details\n Basic Information\n Content\n Introduction and examples of advanced prediction and classification problems in medicine; ML for prognostic and diagnostic tasks; risk scores, time-to-event modeling, survival models, differential diagnosis \u0026amp; population stratification, geometric deep learning: point clouds \u0026amp; meshes, mesh-based segmentation, shape analysis, trustworthy AI in medicine: bias and fairness, generalizability, AI for affordable healthcare, clinical deployment and evaluation, data harmonization, causal inference, transformers, reinforcement learning in medicine, ML for neuro: structural neuroimaging, functional neuroimaging, diffusion imaging, ML for CVD: EEG analysis\n Learning Outcome At the end of the module students should be able to recall advanced topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own AI projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.\n Preconditions IN2403 Artificial Intelligence in Medicine\n","date":1633046400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1633046400,"objectID":"c042b31d695ddbac90198f6d0212a9dd","permalink":"https://compai-lab.io/teaching/aim_lecture_2/","publishdate":"2021-10-01T00:00:00Z","relpermalink":"/teaching/aim_lecture_2/","section":"teaching","summary":"Summer 2022. TUM Informatics. Lecture. [Details](https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950636169\u0026pSpracheNr=2).","tags":["summer"],"title":"Artificial Intelligence in Medicine II (IN2408)","type":"teaching"},{"authors":["Ilkay Oksuz","James R Clough","Bram Ruijsink","Esther Puyol Anton","Aurelien Bustin","Gastao Cruz","Claudia Prieto","Andrew P King","Julia A. Schnabel"],"categories":null,"content":"","date":1606780800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606780800,"objectID":"d19aaabcf1d984f2c0743565f7342a9c","permalink":"https://compai-lab.io/fpublications/pmid-32746141/","publishdate":"2022-06-24T10:22:32.001007Z","relpermalink":"/fpublications/pmid-32746141/","section":"fpublications","summary":"Segmenting anatomical structures in medical images has been successfully addressed with deep learning methods for a range of applications. However, this success is heavily dependent on the quality of the image that is being segmented. A commonly neglected point in the medical image analysis community is the vast amount of clinical images that have severe image artefacts due to organ motion, movement of the patient and/or image acquisition related issues. In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity. The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term. In this paper, we propose to use a segmentation network coupled with this in an end-to-end framework. Our training optimises three different tasks: 1) image artefact detection, 2) artefact correction and 3) image segmentation. We train the reconstruction network to automatically correct for motion-related artefacts using synthetically corrupted cardiac MR k-space data and uncorrected reconstructed images. Using a test set of 500 2D+time cine MR acquisitions from the UK Biobank data set, we achieve demonstrably good image quality and high segmentation accuracy in the presence of synthetic motion artefacts. We showcase better performance compared to various image correction architectures.","tags":null,"title":"Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation","type":"fpublications"},{"authors":["Daniel Rueckert","Julia A. Schnabel"],"categories":null,"content":"","date":1577836800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1577836800,"objectID":"491b67809305ab0438c04df9f7074076","permalink":"https://compai-lab.io/fpublications/8867900/","publishdate":"2022-06-24T10:22:32.000407Z","relpermalink":"/fpublications/8867900/","section":"fpublications","summary":"","tags":null,"title":"Model-Based and Data-Driven Strategies in Medical Image Computing","type":"fpublications"},{"authors":["James R Clough","Nicholas Byrne","Ilkay Oksuz","Veronika Zimmer","Julia A. Schnabel","Andrew P King"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"f443e0153a2d6f19fde7565cf2a49af0","permalink":"https://compai-lab.io/fpublications/clough-2019-topological/","publishdate":"2022-06-24T10:22:31.99995Z","relpermalink":"/fpublications/clough-2019-topological/","section":"fpublications","summary":"","tags":null,"title":"A topological loss function for deep-learning based image segmentation using persistent homology","type":"fpublications"},{"authors":["Ilkay Oksuz","Bram Ruijsink","Esther Puyol-Antón","James R. Clough","Gastao Cruz","Aurelien Bustin","Claudia Prieto","Rene Botnar","Daniel Rueckert","Julia A. Schnabel","Andrew P. King"],"categories":null,"content":"","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"1c41bee1f64cb40ddac9eea7848901e6","permalink":"https://compai-lab.io/fpublications/oksuz-2019136/","publishdate":"2022-06-24T10:22:32.001616Z","relpermalink":"/fpublications/oksuz-2019136/","section":"fpublications","summary":"Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank (UKBB), manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) cine images. We compare two deep learning architectures to classify poor quality CMR images: 1) 3D spatio-temporal Convolutional Neural Networks (3D-CNN), 2) Long-term Recurrent Convolutional Network (LRCN). Though in real clinical setup motion artefacts are common, high-quality imaging of UKBB, which comprises cross-sectional population data of volunteers who do not necessarily have health problems creates a highly imbalanced classification problem. Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space. We also investigate a learning approach using a predetermined curriculum based on synthetic artefact severity. We evaluate our pipeline on a subset of the UK Biobank data set consisting of 3510 CMR images. The LRCN architecture outperformed the 3D-CNN architecture and was able to detect 2D+time short axis images with motion artefacts in less than 1ms with high recall. We compare our approach to a range of state-of-the-art quality assessment methods. The novel data augmentation and curriculum learning approaches both improved classification performance achieving overall area under the ROC curve of 0.89.","tags":["Cardiac MR motion artefacts","Image quality assessment","Artifact","Convolutional neural networks","LSTM"],"title":"Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning","type":"fpublications"},{"authors":["Julia A. Schnabel","Mattias P. Heinrich","Bartłomiej W. Papież","Sir J. Michael Brady"],"categories":null,"content":"","date":1475280000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1475280000,"objectID":"80a82987809cd05557e3ff7d4f5bdb23","permalink":"https://compai-lab.io/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/","publishdate":"2022-06-24T10:22:32.002102Z","relpermalink":"/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/","section":"fpublications","summary":"Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly non-linear, deformable image registration for a wide range of medical applications and imaging modalities, involving the compensation and analysis of physiological organ motion or of tissue changes due to growth or disease patterns. While the original focus of image registration has predominantly been on correcting for rigid-body motion of brain image volumes acquired at different scanning sessions, often with different modalities, the advent of dedicated longitudinal and cross-sectional brain studies soon necessitated the development of more sophisticated methods that are able to detect and measure local structural or functional changes, or group differences. Moving outside of the brain, cine imaging and dynamic imaging required the development of deformable image registration to directly measure or compensate for local tissue motion. Since then, deformable image registration has become a general enabling technology. In this work we will present our own contributions to the state-of-the-art in deformable multi-modal fusion and complex motion modelling, and then discuss remaining challenges and provide future perspectives to the field.","tags":["Demons","Discrete optimization","Registration uncertainty","Sliding motion","Supervoxels","Multi-modality"],"title":"Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling","type":"fpublications"},{"authors":["Mattias P Heinrich","Mark Jenkinson","Manav Bhushan","Tahreema Matin","Fergus V Gleeson","Michael Brady","Julia A. Schnabel"],"categories":null,"content":"","date":1325376000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1325376000,"objectID":"c285d6052f6a1404711a98600f07ff83","permalink":"https://compai-lab.io/fpublications/heinrich-2012-mind/","publishdate":"2022-06-24T10:22:31.99789Z","relpermalink":"/fpublications/heinrich-2012-mind/","section":"fpublications","summary":"","tags":null,"title":"MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration","type":"fpublications"},{"authors":["Daniel Rueckert","Alejandro F Frangi","Julia A. Schnabel"],"categories":null,"content":"","date":1041379200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1041379200,"objectID":"bcc52780246602a229b208580b80bea5","permalink":"https://compai-lab.io/fpublications/rueckert-2003-automatic/","publishdate":"2022-06-24T10:04:06.366031Z","relpermalink":"/fpublications/rueckert-2003-automatic/","section":"fpublications","summary":"","tags":null,"title":"Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration","type":"fpublications"},{"authors":["Julia A. Schnabel","Daniel Rueckert","Marcel Quist","Jane M Blackall","Andy D Castellano-Smith","Thomas Hartkens","Graeme P Penney","Walter A Hall","Haiying Liu","Charles L Truwit"," others"],"categories":null,"content":"","date":978307200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":978307200,"objectID":"8deac30695c3cd5d32ac51b194bcdf89","permalink":"https://compai-lab.io/fpublications/schnabel-2001-generic/","publishdate":"2022-06-24T10:22:31.999409Z","relpermalink":"/fpublications/schnabel-2001-generic/","section":"fpublications","summary":"","tags":null,"title":"A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations","type":"fpublications"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6d99026b9e19e4fa43d5aadf147c7176","permalink":"https://compai-lab.io/contact/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/contact/","section":"","summary":"","tags":null,"title":"","type":"widget_page"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"b0d61e5cbb7472bf320bf0ef2aaeb977","permalink":"https://compai-lab.io/tour/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/tour/","section":"","summary":"","tags":null,"title":"Tour","type":"widget_page"}] \ No newline at end of file diff --git a/index.xml b/index.xml new file mode 100644 index 0000000..3daed7c --- /dev/null +++ b/index.xml @@ -0,0 +1,589 @@ + + + + Computational Imaging and AI in Medicine + https://compai-lab.io/ + + Computational Imaging and AI in Medicine + Wowchemy (https://wowchemy.com)en-usSat, 01 Jun 2030 13:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + Computational Imaging and AI in Medicine + https://compai-lab.io/ + + + + Example Event + https://compai-lab.io/event/example/ + Sat, 01 Jun 2030 13:00:00 +0000 + https://compai-lab.io/event/example/ + <p>Slides can be added in a few ways:</p> +<ul> +<li><strong>Create</strong> slides using Wowchemy&rsquo;s <a href="https://wowchemy.com/docs/managing-content/#create-slides" target="_blank" rel="noopener"><em>Slides</em></a> feature and link using <code>slides</code> parameter in the front matter of the talk file</li> +<li><strong>Upload</strong> an existing slide deck to <code>static/</code> and link using <code>url_slides</code> parameter in the front matter of the talk file</li> +<li><strong>Embed</strong> your slides (e.g. Google Slides) or presentation video on this page using <a href="https://wowchemy.com/docs/writing-markdown-latex/" target="_blank" rel="noopener">shortcodes</a>.</li> +</ul> +<p>Further event details, including page elements such as image galleries, can be added to the body of this page.</p> + + + + + Review paper accepted at IEEE Transactions on Medical Imaging + https://compai-lab.io/post/spieker_eichhorn_tmi/ + Wed, 25 Oct 2023 00:00:00 +0000 + https://compai-lab.io/post/spieker_eichhorn_tmi/ + <p><em>Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review</em> by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.</p> +<p>Motion remains a major challenge in MRI and various deep learning solutions have been proposed – but what are common challenges and potentials? Check out <a href="https://ieeexplore.ieee.org/document/10285512" target="_blank" rel="noopener">this review</a>, which identifies differences and synergies of recent methods and bridges the gap between AI and MR physics.</p> + + + + Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + Fri, 13 Oct 2023 00:00:00 +0000 + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + + + + + Five papers accepted at MICCAI 2023 workshops + https://compai-lab.io/post/iml_miccai_workshops/ + Thu, 14 Sep 2023 00:00:00 +0000 + https://compai-lab.io/post/iml_miccai_workshops/ + <p>Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p>Interested to hear more about our work? Then join us at the following workshops:</p> +<ul> +<li> +<p>Veronika Spieker will be at the <a href="https://dgm4miccai.github.io/" target="_blank" rel="noopener">DGM4</a> workshop to talk about <a href="https://arxiv.org/abs/2308.08830" target="_blank" rel="noopener">Neural Implicit Representations for Abdominal MR Reconstruction</a> on October 8, at 10:25.</p> +</li> +<li> +<p>Hannah Eichhorn presents her work on physics-aware motion simulation for T2*-weighted MRI at the <a href="https://2023.sashimi-workshop.org/program/" target="_blank" rel="noopener">SASHIMI</a> workshop on October 8, at 14:40. Check out the <a href="https://arxiv.org/abs/2303.10987" target="_blank" rel="noopener">preprint</a> for more information!</p> +</li> +<li> +<p>Maxime Di Folco presents at the <a href="https://stacom.github.io/stacom2023/" target="_blank" rel="noopener">STACOM</a> workshop on October 12, at 11:15 the work of Josh Stein on &ldquo;Sparse annotation strategies for segmentation of short axis cardiac MRI&rdquo; (<a href="https://arxiv.org/abs/2307.12619" target="_blank" rel="noopener">preprint</a>).</p> +</li> +<li> +<p>Cosmin Bercea will talk about <a href="https://arxiv.org/pdf/2308.13861.pdf" target="_blank" rel="noopener">Bias in Unsupervised Anomaly Detection</a> at the <a href="https://faimi-workshop.github.io/2023-miccai/" target="_blank" rel="noopener">FAIMI</a> workshop on October 12, at 2:50 PDT.</p> +</li> +<li> +<p>Daniel Lang will talk about <a href="https://arxiv.org/abs/2303.05861" target="_blank" rel="noopener">Anomaly Detection in Non-Contrast Enhanced Breast MRI</a> at the <a href="https://caption-workshop.github.io/miccai2023/#Workshop%20sessions" target="_blank" rel="noopener">CaPTion</a> workshop on October 12.</p> +</li> +</ul> + + + + Learning of and on manifolds in medical imaging (IN2107) + https://compai-lab.io/teaching/manifold_seminar/ + Wed, 19 Jul 2023 00:00:00 +0000 + https://compai-lab.io/teaching/manifold_seminar/ + <p><a href="https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950706204" target="_blank" rel="noopener">Course details</a></p> +<p>Considering the manifold of medical imaging data, i.e. the underlying topological space, facilitates the analysis, interpretation, and visualization of the data. This seminar focuses on machine and deep learning methods that either learn the manifold from high-dimensional data or use manifold-valued data as input. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed. This includes, but is not +limited to:</p> +<ul> +<li>Introduction to manifolds</li> +<li>Difference between learning on and of a manifold</li> +<li>Examples of manifold-valued data in medical imaging</li> +<li>State-of-the-art methods for manifold-valued data</li> +<li>Clinical applications</li> +</ul> +<p>Please register to: <a href="https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx" target="_blank" rel="noopener">https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx</a></p> +<p>Check the intro slides here: + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/files/Manifold_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<object data="/files/Manifold_seminar.pdf" type="application/pdf" width="100%" height="400"> +</object> + + + + + Unsupervised Anomaly Detection in Medical Imaging + https://compai-lab.io/teaching/anomaly_seminar/ + Wed, 19 Jul 2023 00:00:00 +0000 + https://compai-lab.io/teaching/anomaly_seminar/ + <p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/autoddpm_teaser.gif" alt="Teaser" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>Anomaly detection aims to identify patterns that do not conform to the expected normal distribution. Despite its importance for clinical applications, the detection of outliers is still a very challenging task due to the rarity, unknownness, diversity, and heterogeneity of anomalies. Basic problem formulations to recent advances in the field will be discussed.</p> +<p>This includes, but is not limited to:</p> +<ul> +<li>Reconstruction-based anomaly segmentation</li> +<li>Probabilistic models, i.e., anomaly likelihood estimation</li> +<li>Generative models</li> +<li>Self-supervised-, contrastive methods</li> +<li>Unsupervised methods</li> +<li>Clinical Applications</li> +</ul> +<p>Please register via the TUM matching system: <a href="https://matching.in.tum.de" target="_blank" rel="noopener">https://matching.in.tum.de</a></p> +<p>Check the intro slides here: + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/files/UAD_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<object data="/files/UAD_seminar.pdf" type="application/pdf" width="100%" height="400"> +</object> + + + + + Exploring Riemannian Manifolds for Medical Image Classification (f/m/x) + https://compai-lab.io/vacancies/msc_manifold_anna/ + Sat, 08 Jul 2023 00:00:00 +0000 + https://compai-lab.io/vacancies/msc_manifold_anna/ + <p>Abstract:</p> +<p>This Master’s project aims to explore the use of covariance descriptors for disease classification with medical +images. First, the MedMNIST toy dataset will be explored. Then, the student will work with an open-source +medical dataset, e.g. of 2D chest x-ray or 3D cardiac MR images</p> + + + + + Two papers accepted at MICCAI 2023 + https://compai-lab.io/post/bercea_miccai/ + Fri, 26 May 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_miccai/ + <p>&ldquo;<em>What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection</em> and <em>Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection</em> by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/morphaeus.gif" alt="MorphAEus" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<ul> +<li>Curios what auto-encoders actually learn? Check out <a href="https://ci.bercea.net/project/morphaeus/" target="_blank" rel="noopener">this</a> project page to find out more.</li> +</ul> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/phanes.gif" alt="PHANES" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<ul> +<li>How can we reverse anomalies in medical images? Check out the project <a href="https://ci.bercea.net/project/phanes/" target="_blank" rel="noopener">here</a>.</li> +</ul> + + + + Paper accepted at ICML IMLH 2023 + https://compai-lab.io/post/bercea_icml/ + Thu, 25 May 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_icml/ + <p>We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!</p> +<p>Curious about how to solve the noise paradox illustrated below? Check out our <a href="https://ci.bercea.net/project/autoddpm/" target="_blank" rel="noopener">project page</a>.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/noise_paradox.gif" alt="AutoDDPM" loading="lazy" data-zoomable /></div> + </div></figure> +</p> + + + + Paper accepted at MIDL 2023 (oral talk) + https://compai-lab.io/post/bercea_midl/ + Fri, 28 Apr 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_midl/ + <p>&ldquo;<em>Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening</em>&rdquo; by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/ra.png" alt="RA" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>Moving beyond hyperintensity thresholding: This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection. Our proposed method RA outperformed SOTA methods on T1w brain MRIs, detecting more global anomalies (AUROC increased from 73.1 to 89.4) and local pathologies (detection rate increased from 52.6% to 86.0%).</p> +<p>Want to know more? Check the <a href="https://ci.bercea.net/project/ra/" target="_blank" rel="noopener">project site</a>.</p> + + + + Abstracts accepted at 2023 ISMRM & ISMRT Annual Meeting + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + Tue, 25 Apr 2023 00:00:00 +0000 + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + <p>Veronika Spieker&rsquo;s and Hannah Eichhorn&rsquo;s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM &amp; ISMRT Annual Meeting.</p> +<p>Veronika Spieker will present her work on &ldquo;<em>Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction</em>&rdquo; on Monday, 05 June 2023 at 1:45 pm EDT.</p> +<p>Hannah Eichhorn will present her work on &ldquo;<em>Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations</em>&rdquo; on Tuesday, 06 June 2023 at 4:45 pm EDT. Check <a href="https://github.com/HannahEichhorn/T2starRealisticMotionSimulation" target="_blank" rel="noopener">this GitHub repository</a> for more information.</p> + + + + + Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x) + https://compai-lab.io/vacancies/msc_surface/ + Mon, 21 Nov 2022 00:00:00 +0000 + https://compai-lab.io/vacancies/msc_surface/ + <p>Abstract:</p> +<p>In recent years, unsupervised and semi-supervised learning from populations of surfaces and curves has received a lot of attention. Such data representations are analyzed according to their shapes which open a broad range of applications in machine learning, robotics, statistics and engineering. In particular, studying the shape of surfaces have become an important tool in biology and medical imaging. The extraction of appropriate data representations, such as triangulated surfaces, is crucial for the subsequent analysis. These surfaces are for example obtained from binary segmentations or 3D point clouds. Using standard methods, such surfaces are often not very accurate and require several post-processing steps, such as smoothing and simplifications. +Deep learning based methods are of great interest in various fields such as medical imaging, com- puter vision, applied mathematics and are successfully used in the field of image segmentation. Gener- ally, a specific formulation requires a particular attention to representations, loss functions, probability models, optimization techniques, etc. This choice is very crucial due to the underlying geometry on the space of representations and constraints. we aim to develop a new set of automatic methods that can compute a triangulation and a normal field from a 3D dataset (binary image and/or 3D point cloud). +The goal of this project is to understand the-state-of-the-art methods (e.g., [?]) and to propose solutions in the context of constructing a mesh from 3D images/point sets. We are interested in learn- ing from a dataset of smooth surfaces and their corresponding 3D datasets to make the triangulation or resampling accurate. The application will be the extraction of a smooth surfaces from μ-CT and CT data of the cochlea and inner ear, whose shapes can then be analyzed subsequently for population studies. +To summarize, the key steps are : (i) Literature review and getting familiar with some state-of- the-art methods in the medical context; (ii) Implementing and testing the code before validation on real data; (iii) Optimizing the code and comparing with baseline methods. If successful, the method would be applied to analyze and classify surfaces.</p> + + + + + New publication at Nature Machine Intelligence + https://compai-lab.io/post/bercea_nature/ + Tue, 02 Aug 2022 00:00:00 +0000 + https://compai-lab.io/post/bercea_nature/ + <p><em>Federated disentangled representation learning for unsupervised brain anomaly detection</em> by Cosmin I. Bercea et al. has been published at Nature Machine Intelligence.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/feddis.png" alt="Feddis" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>In this work, a federated algorithm was trained on more than 1,500 MR scans of healthy study participants from four institutions while maintaining data privacy with the goal to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before.</p> +<p>Check the <a href="https://ci.bercea.net/project/feddis/" target="_blank" rel="noopener">project site</a> for more information.</p> + + + + What do we learn? Debunking the Myth of Unsupervised Outlier Detection + https://compai-lab.io/publication/bercea-2022-we/ + Wed, 08 Jun 2022 00:00:00 +0000 + https://compai-lab.io/publication/bercea-2022-we/ + <div class="alert alert-note"> + <div> + Click the <em>Cite</em> button above to demo the feature to enable visitors to import publication metadata into their reference management software. + </div> +</div> + + + + + Veronika Spieker wins the 1st place MedtecLIVE Talent Award 2022 + https://compai-lab.io/post/spieker_award/ + Mon, 30 May 2022 00:00:00 +0000 + https://compai-lab.io/post/spieker_award/ + <p>The MedtecLIVE Talent Award 2022 is given to bachelor&rsquo;s and master&rsquo;s theses that relate to an innovation, improvement, or new application in medical technology along with its entire value chain.</p> +<p>After a first screening of her thesis abstract, Veronika was invited to the live finale in Stuttgart to present her thesis in an 8-minute pitch. The extensiveness of her work, her drive to clinical translation as well as visual and interactive presentation convinced the jury to award her the first prize.</p> +<p>As part of her M.Sc. in Medical Technologies at TUM, Veronika conducted her master thesis at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and published her results in Sensors (<a href="https://www.mdpi.com/1424-8220/21/21/7404%29" target="_blank" rel="noopener">www.mdpi.com/1424-8220/21/21/7404)</a>.</p> +<p>We are happy, that she is now pursuing her PhD at our lab at Helmholtz Munich!</p> +<p>More information on the finale can be found here:</p> +<ul> +<li> +<p><a href="https://medizin-und-technik.industrie.de/medizintechnik-studium/talent-award-zur-medtec-live-with-t4m-jetzt-ist-der-nachwuchs-dran/" target="_blank" rel="noopener">https://medizin-und-technik.industrie.de/medizintechnik-studium/talent-award-zur-medtec-live-with-t4m-jetzt-ist-der-nachwuchs-dran/</a></p> +</li> +<li> +<p><a href="https://www.mirmi.tum.de/mirmi/news/article/veronika-spieker-is-honored-with-the-1st-place-medteclive-talent-award-2022/" target="_blank" rel="noopener">https://www.mirmi.tum.de/mirmi/news/article/veronika-spieker-is-honored-with-the-1st-place-medteclive-talent-award-2022/</a></p> +</li> +</ul> + + + + A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis + https://compai-lab.io/publication/machado-2022-deep/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/publication/machado-2022-deep/ + + + + + AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information + https://compai-lab.io/publication/li-2022-atrialjsqnet/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/publication/li-2022-atrialjsqnet/ + + + + + Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning + https://compai-lab.io/publication/dal-2022-improved/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/publication/dal-2022-improved/ + + + + + Medical Image Registation I (IN2107) + https://compai-lab.io/teaching/master_seminar/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/teaching/master_seminar/ + <p><a href="https://campus.tum.de/tumonline/pl/ui/$ctx/wbLv.wbShowLVDetail?pStpSpNr=950627128" target="_blank" rel="noopener">Course details</a></p> +<p>Image registration is the process of aligning two or more images, and crucial for many image analysis pipelines. This seminar will cover selected material of image registration for medical imaging. Basic problem formulations to recent advances in the field will be discussed. This includes, but is not limited to:</p> +<ul> +<li>Learning and non-learning based image registration</li> +<li>Optimization techniques</li> +<li>Image registration for multi-modal data</li> +<li>Multi-resolution and regularization strategies</li> +<li>Linear and non-linear deformations</li> +<li>Supervised and unsupervised learning</li> +<li>Clinical applications</li> +</ul> + + + + + PhD AI-enabled medical imaging (f/m/x) + https://compai-lab.io/vacancies/phd-job/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/vacancies/phd-job/ + + + + + Artificial Intelligence in Medicine (IN2403) + https://compai-lab.io/teaching/aim_lecture/ + Fri, 01 Oct 2021 00:00:00 +0000 + https://compai-lab.io/teaching/aim_lecture/ + <ul> +<li><a href="https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950596772" target="_blank" rel="noopener">Course Details</a></li> +<li><a href="https://www.ph.tum.de/academics/org/cc/mh/IN2403/" target="_blank" rel="noopener">Basic Information</a></li> +</ul> +<p>At the end of the module students should be able to recall the important topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own deep learning projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.</p> +<ul> +<li>Introduction: Clinical motivation, clinical data, clinical workflows</li> +<li>ML for medical imaging• Data curation for medical applications</li> +<li>Domain shift in medical applications: Adversarial learning and Transfer learning</li> +<li>Self-supervised learning and unsupervised learning</li> +<li>Learning from sparse and noisy data</li> +<li>ML for unstructured and multi-modal clinical data</li> +<li>NLP for clinical data• Bayesian approaches to deep learning and uncertainty</li> +<li>Interpretability and explainability</li> +<li>Federated learning, privacy-preserving ML and ethics</li> +<li>ML for time-to-event modeling, survival models</li> +<li>ML for differential diagnosis and stratification• Clinical applications in pathology/radiology/omics</li> +</ul> + + + + + Artificial Intelligence in Medicine II (IN2408) + https://compai-lab.io/teaching/aim_lecture_2/ + Fri, 01 Oct 2021 00:00:00 +0000 + https://compai-lab.io/teaching/aim_lecture_2/ + <ul> +<li> +<p><a href="https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950636169&amp;pSpracheNr=2" target="_blank" rel="noopener">Course Details</a></p> +</li> +<li> +<p><a href="https://www.ph.tum.de/academics/org/cc/course/950636169/" target="_blank" rel="noopener">Basic Information</a></p> +</li> +<li> +<p>Content</p> +</li> +</ul> +<p>Introduction and examples of advanced prediction and classification problems in medicine; ML for prognostic and diagnostic tasks; risk scores, time-to-event modeling, survival models, differential diagnosis &amp; population stratification, geometric deep learning: point clouds &amp; meshes, mesh-based segmentation, shape analysis, trustworthy AI in medicine: bias and fairness, generalizability, AI for affordable healthcare, clinical deployment and evaluation, data harmonization, causal inference, transformers, reinforcement learning in medicine, ML for neuro: structural neuroimaging, functional neuroimaging, diffusion imaging, ML for CVD: EEG analysis</p> +<ul> +<li>Learning Outcome</li> +</ul> +<p>At the end of the module students should be able to recall advanced topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own AI projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.</p> +<ul> +<li>Preconditions</li> +</ul> +<p>IN2403 Artificial Intelligence in Medicine</p> + + + + + Deep Learning-Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation + https://compai-lab.io/fpublications/pmid-32746141/ + Tue, 01 Dec 2020 00:00:00 +0000 + https://compai-lab.io/fpublications/pmid-32746141/ + + + + + Model-Based and Data-Driven Strategies in Medical Image Computing + https://compai-lab.io/fpublications/8867900/ + Wed, 01 Jan 2020 00:00:00 +0000 + https://compai-lab.io/fpublications/8867900/ + + + + + A topological loss function for deep-learning based image segmentation using persistent homology + https://compai-lab.io/fpublications/clough-2019-topological/ + Tue, 01 Jan 2019 00:00:00 +0000 + https://compai-lab.io/fpublications/clough-2019-topological/ + + + + + Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning + https://compai-lab.io/fpublications/oksuz-2019136/ + Tue, 01 Jan 2019 00:00:00 +0000 + https://compai-lab.io/fpublications/oksuz-2019136/ + + + + + Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling + https://compai-lab.io/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/ + Sat, 01 Oct 2016 00:00:00 +0000 + https://compai-lab.io/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/ + + + + + MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration + https://compai-lab.io/fpublications/heinrich-2012-mind/ + Sun, 01 Jan 2012 00:00:00 +0000 + https://compai-lab.io/fpublications/heinrich-2012-mind/ + + + + + Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration + https://compai-lab.io/fpublications/rueckert-2003-automatic/ + Wed, 01 Jan 2003 00:00:00 +0000 + https://compai-lab.io/fpublications/rueckert-2003-automatic/ + + + + + A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations + https://compai-lab.io/fpublications/schnabel-2001-generic/ + Mon, 01 Jan 2001 00:00:00 +0000 + 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a=e8(e.instance.popper,e.instance.reference,i.padding,i.boundariesElement,e.positionFixed),t=e.placement.split("-")[0],r=W(t),n=e.placement.split("-")[1]||"",s=[];switch(i.behavior){case"flip":s=[t,r];break;case"clockwise":s=ez(t);break;case"counterclockwise":s=ez(t,!0);break;default:s=i.behavior}return s.forEach(function(w,j){if(t!==w||s.length===j+1)return e;t=e.placement.split("-")[0],r=W(t);var 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s=e.placement,t=s.split("-")[0],o=e.offsets,n=o.popper,a=o.reference,i=-1!==["left","right"].indexOf(t),r=-1===["top","left"].indexOf(t);return n[i?"left":"top"]=a[t]-(r?n[i?"width":"height"]:0),e.placement=W(s),e.offsets.popper=l(n),e}},hide:{order:800,enabled:!0,fn:function(e){if(!eM(e.instance.modifiers,"hide","preventOverflow"))return e;var t=e.offsets.reference,n=S(e.instance.modifiers,function(e){return"preventOverflow"===e.name}).boundaries;if(t.bottomn.right||t.top>n.bottom||t.right2&&void 0!==arguments[2]?arguments[2]:{};tW(this,e),this.scheduleUpdate=function(){return 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2023 | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Paper accepted at ICML IMLH 2023

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We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!

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Curious about how to solve the noise paradox illustrated below? Check out our project page.

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AutoDDPM
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Cosmin I. Bercea
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PhD Student
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My research interests include interpretable machine learning for anomaly detection.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Two papers accepted at MICCAI 2023

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+ + + + + + + + + + May 26, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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“What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection and Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

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MorphAEus
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  • Curios what auto-encoders actually learn? Check out this project page to find out more.
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PHANES
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  • How can we reverse anomalies in medical images? Check out the project here.
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+ + + Cosmin I. Bercea + + +
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Cosmin I. Bercea
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PhD Student
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My research interests include interpretable machine learning for anomaly detection.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Paper accepted at MIDL 2023 (oral talk)

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“Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening” by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.

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RA
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Moving beyond hyperintensity thresholding: This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection. Our proposed method RA outperformed SOTA methods on T1w brain MRIs, detecting more global anomalies (AUROC increased from 73.1 to 89.4) and local pathologies (detection rate increased from 52.6% to 86.0%).

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Want to know more? Check the project site.

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Cosmin I. Bercea
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PhD Student
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My research interests include interpretable machine learning for anomaly detection.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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New publication at Nature Machine Intelligence

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+ + + + Cosmin I. Bercea +
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Federated disentangled representation learning for unsupervised brain anomaly detection by Cosmin I. Bercea et al. has been published at Nature Machine Intelligence.

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Feddis
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In this work, a federated algorithm was trained on more than 1,500 MR scans of healthy study participants from four institutions while maintaining data privacy with the goal to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before.

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Check the project site for more information.

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+ + + Cosmin I. Bercea + + +
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Cosmin I. Bercea
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PhD Student
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My research interests include interpretable machine learning for anomaly detection.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Five papers accepted at MICCAI 2023 workshops

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+ + + + + + + + + + + + + + + + Sep 14, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

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Interested to hear more about our work? Then join us at the following workshops:

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Hannah Eichhorn
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PhD student
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Hannah Eichhorn’s research focuses on deep learning-based reconstruction of multi-parametric brain MRI.

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+ + + Veronika Spieker + + +
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Veronika Spieker
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PhD Student
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Veronika Spieker’s interests include AI-based methods for MR reconstrution and motion correction.

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Cosmin I. Bercea
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PhD Student
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My research interests include interpretable machine learning for anomaly detection.

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Daniel M. Lang
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Research Scientist
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My research focuses on the application of deep learning models for problem settings in cancer imaging.

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+ + + Maxime Di Folco + + +
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Maxime Di Folco
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Research Scientist
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My research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods that aim to acquire low dimensional representation of high dimensional data. I have a strong interest in cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Posts

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+ Review paper accepted at IEEE Transactions on Medical Imaging +
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Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.

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+ + + + + + + + + + + + + + + + Oct 25, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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+ Five papers accepted at MICCAI 2023 workshops +
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Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

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Interested to hear more about our work? Then join us at the following workshops:

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+ + + + + + + + + + + + + + + + Sep 14, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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+ + + + + Five papers accepted at MICCAI 2023 workshops + + +
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+ Two papers accepted at MICCAI 2023 +
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“What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection and Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.

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+ Paper accepted at ICML IMLH 2023 +
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We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!

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+ + + + + + + + + + May 25, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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+ + + + + Paper accepted at ICML IMLH 2023 + + +
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+ Paper accepted at MIDL 2023 (oral talk) +
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“Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening” by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.

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+ + + + + + + + + + Apr 28, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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+ Abstracts accepted at 2023 ISMRM & ISMRT Annual Meeting +
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+ Veronika Spieker’s and Hannah Eichhorn’s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM & ISMRT Annual Meeting. +Veronika Spieker will present her work on “Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction” on Monday, 05 June 2023 at 1:45 pm EDT. +
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+ New publication at Nature Machine Intelligence +
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Federated disentangled representation learning for unsupervised brain anomaly detection by Cosmin I. Bercea et al. has been published at Nature Machine Intelligence.

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+ Veronika Spieker wins the 1st place MedtecLIVE Talent Award 2022 +
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The MedtecLIVE Talent Award 2022 is given to bachelor’s and master’s theses that relate to an innovation, improvement, or new application in medical technology along with its entire value chain.

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+ + + + + + + + + + + May 30, 2022 + + + + + + + + + 1 min read + + + + + + + + + + + +
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+ + + + + Veronika Spieker wins the 1st place MedtecLIVE Talent Award 2022 + + +
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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/post/index.xml b/post/index.xml new file mode 100644 index 0000000..f6c612b --- /dev/null +++ b/post/index.xml @@ -0,0 +1,230 @@ + + + + Posts | Computational Imaging and AI in Medicine + https://compai-lab.io/post/ + + Posts + Wowchemy (https://wowchemy.com)en-usWed, 25 Oct 2023 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + Posts + https://compai-lab.io/post/ + + + + Review paper accepted at IEEE Transactions on Medical Imaging + https://compai-lab.io/post/spieker_eichhorn_tmi/ + Wed, 25 Oct 2023 00:00:00 +0000 + https://compai-lab.io/post/spieker_eichhorn_tmi/ + <p><em>Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review</em> by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.</p> +<p>Motion remains a major challenge in MRI and various deep learning solutions have been proposed – but what are common challenges and potentials? Check out <a href="https://ieeexplore.ieee.org/document/10285512" target="_blank" rel="noopener">this review</a>, which identifies differences and synergies of recent methods and bridges the gap between AI and MR physics.</p> + + + + Five papers accepted at MICCAI 2023 workshops + https://compai-lab.io/post/iml_miccai_workshops/ + Thu, 14 Sep 2023 00:00:00 +0000 + https://compai-lab.io/post/iml_miccai_workshops/ + <p>Five papers have been accepted for publication at workshops associated with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p>Interested to hear more about our work? Then join us at the following workshops:</p> +<ul> +<li> +<p>Veronika Spieker will be at the <a href="https://dgm4miccai.github.io/" target="_blank" rel="noopener">DGM4</a> workshop to talk about <a href="https://arxiv.org/abs/2308.08830" target="_blank" rel="noopener">Neural Implicit Representations for Abdominal MR Reconstruction</a> on October 8, at 10:25.</p> +</li> +<li> +<p>Hannah Eichhorn presents her work on physics-aware motion simulation for T2*-weighted MRI at the <a href="https://2023.sashimi-workshop.org/program/" target="_blank" rel="noopener">SASHIMI</a> workshop on October 8, at 14:40. Check out the <a href="https://arxiv.org/abs/2303.10987" target="_blank" rel="noopener">preprint</a> for more information!</p> +</li> +<li> +<p>Maxime Di Folco presents at the <a href="https://stacom.github.io/stacom2023/" target="_blank" rel="noopener">STACOM</a> workshop on October 12, at 11:15 the work of Josh Stein on &ldquo;Sparse annotation strategies for segmentation of short axis cardiac MRI&rdquo; (<a href="https://arxiv.org/abs/2307.12619" target="_blank" rel="noopener">preprint</a>).</p> +</li> +<li> +<p>Cosmin Bercea will talk about <a href="https://arxiv.org/pdf/2308.13861.pdf" target="_blank" rel="noopener">Bias in Unsupervised Anomaly Detection</a> at the <a href="https://faimi-workshop.github.io/2023-miccai/" target="_blank" rel="noopener">FAIMI</a> workshop on October 12, at 2:50 PDT.</p> +</li> +<li> +<p>Daniel Lang will talk about <a href="https://arxiv.org/abs/2303.05861" target="_blank" rel="noopener">Anomaly Detection in Non-Contrast Enhanced Breast MRI</a> at the <a href="https://caption-workshop.github.io/miccai2023/#Workshop%20sessions" target="_blank" rel="noopener">CaPTion</a> workshop on October 12.</p> +</li> +</ul> + + + + Two papers accepted at MICCAI 2023 + https://compai-lab.io/post/bercea_miccai/ + Fri, 26 May 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_miccai/ + <p>&ldquo;<em>What Do AEs Learn? Challenging Common Assumptions in Unsupervised Anomaly Detection</em> and <em>Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection</em> by Cosmin I. Bercea et al. have been accepted for publication at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, which will be held from October 8th to 12th 2023 in Vancouver, Canada.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/morphaeus.gif" alt="MorphAEus" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<ul> +<li>Curios what auto-encoders actually learn? Check out <a href="https://ci.bercea.net/project/morphaeus/" target="_blank" rel="noopener">this</a> project page to find out more.</li> +</ul> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/phanes.gif" alt="PHANES" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<ul> +<li>How can we reverse anomalies in medical images? Check out the project <a href="https://ci.bercea.net/project/phanes/" target="_blank" rel="noopener">here</a>.</li> +</ul> + + + + Paper accepted at ICML IMLH 2023 + https://compai-lab.io/post/bercea_icml/ + Thu, 25 May 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_icml/ + <p>We are delighted to announce that our research on developing automatic diffusion models for anomaly detection has been accepted and will be published in the proceedings of the 3rd workshop for Interpretable Machine Learning in Healthcare, held at the International Conference on Machine Learning 2023. Congratulations to our dedicated student Michael for his outstanding contribution to this achievement!</p> +<p>Curious about how to solve the noise paradox illustrated below? Check out our <a href="https://ci.bercea.net/project/autoddpm/" target="_blank" rel="noopener">project page</a>.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/noise_paradox.gif" alt="AutoDDPM" loading="lazy" data-zoomable /></div> + </div></figure> +</p> + + + + Paper accepted at MIDL 2023 (oral talk) + https://compai-lab.io/post/bercea_midl/ + Fri, 28 Apr 2023 00:00:00 +0000 + https://compai-lab.io/post/bercea_midl/ + <p>&ldquo;<em>Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening</em>&rdquo; by Cosmin I. Bercea et al. has been accepted for publication at Medical Imaging with Deep Learning, Nashville, 2023. Cosmin Bercea will present his work on Monday, 10 July 2023 at 9:15 pm CEST.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/ra.png" alt="RA" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>Moving beyond hyperintensity thresholding: This paper analyzes the challenges and outlines opportunities for advancing the field of unsupervised anomaly detection. Our proposed method RA outperformed SOTA methods on T1w brain MRIs, detecting more global anomalies (AUROC increased from 73.1 to 89.4) and local pathologies (detection rate increased from 52.6% to 86.0%).</p> +<p>Want to know more? Check the <a href="https://ci.bercea.net/project/ra/" target="_blank" rel="noopener">project site</a>.</p> + + + + Abstracts accepted at 2023 ISMRM & ISMRT Annual Meeting + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + Tue, 25 Apr 2023 00:00:00 +0000 + https://compai-lab.io/post/spieker_eichhorn_ismrm/ + <p>Veronika Spieker&rsquo;s and Hannah Eichhorn&rsquo;s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM &amp; ISMRT Annual Meeting.</p> +<p>Veronika Spieker will present her work on &ldquo;<em>Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction</em>&rdquo; on Monday, 05 June 2023 at 1:45 pm EDT.</p> +<p>Hannah Eichhorn will present her work on &ldquo;<em>Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations</em>&rdquo; on Tuesday, 06 June 2023 at 4:45 pm EDT. Check <a href="https://github.com/HannahEichhorn/T2starRealisticMotionSimulation" target="_blank" rel="noopener">this GitHub repository</a> for more information.</p> + + + + + New publication at Nature Machine Intelligence + https://compai-lab.io/post/bercea_nature/ + Tue, 02 Aug 2022 00:00:00 +0000 + https://compai-lab.io/post/bercea_nature/ + <p><em>Federated disentangled representation learning for unsupervised brain anomaly detection</em> by Cosmin I. Bercea et al. has been published at Nature Machine Intelligence.</p> +<p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/feddis.png" alt="Feddis" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>In this work, a federated algorithm was trained on more than 1,500 MR scans of healthy study participants from four institutions while maintaining data privacy with the goal to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before.</p> +<p>Check the <a href="https://ci.bercea.net/project/feddis/" target="_blank" rel="noopener">project site</a> for more information.</p> + + + + Veronika Spieker wins the 1st place MedtecLIVE Talent Award 2022 + https://compai-lab.io/post/spieker_award/ + Mon, 30 May 2022 00:00:00 +0000 + https://compai-lab.io/post/spieker_award/ + <p>The MedtecLIVE Talent Award 2022 is given to bachelor&rsquo;s and master&rsquo;s theses that relate to an innovation, improvement, or new application in medical technology along with its entire value chain.</p> +<p>After a first screening of her thesis abstract, Veronika was invited to the live finale in Stuttgart to present her thesis in an 8-minute pitch. The extensiveness of her work, her drive to clinical translation as well as visual and interactive presentation convinced the jury to award her the first prize.</p> +<p>As part of her M.Sc. in Medical Technologies at TUM, Veronika conducted her master thesis at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and published her results in Sensors (<a href="https://www.mdpi.com/1424-8220/21/21/7404%29" target="_blank" rel="noopener">www.mdpi.com/1424-8220/21/21/7404)</a>.</p> +<p>We are happy, that she is now pursuing her PhD at our lab at Helmholtz Munich!</p> +<p>More information on the finale can be found here:</p> +<ul> +<li> +<p><a href="https://medizin-und-technik.industrie.de/medizintechnik-studium/talent-award-zur-medtec-live-with-t4m-jetzt-ist-der-nachwuchs-dran/" target="_blank" rel="noopener">https://medizin-und-technik.industrie.de/medizintechnik-studium/talent-award-zur-medtec-live-with-t4m-jetzt-ist-der-nachwuchs-dran/</a></p> +</li> +<li> +<p><a href="https://www.mirmi.tum.de/mirmi/news/article/veronika-spieker-is-honored-with-the-1st-place-medteclive-talent-award-2022/" target="_blank" rel="noopener">https://www.mirmi.tum.de/mirmi/news/article/veronika-spieker-is-honored-with-the-1st-place-medteclive-talent-award-2022/</a></p> +</li> +</ul> + + + + diff --git a/post/page/1/index.html b/post/page/1/index.html new file mode 100644 index 0000000..3bf2fbc --- /dev/null +++ b/post/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/post/ + + + + + + diff --git a/post/spieker_award/featured.jpg b/post/spieker_award/featured.jpg new file mode 100644 index 0000000..ff02535 Binary files /dev/null and b/post/spieker_award/featured.jpg differ diff --git a/post/spieker_award/featured_hu34805b7071e74a97df17d3392ad52d03_143962_150x0_resize_q75_h2_lanczos.webp b/post/spieker_award/featured_hu34805b7071e74a97df17d3392ad52d03_143962_150x0_resize_q75_h2_lanczos.webp new file mode 100644 index 0000000..c0f6b21 Binary files /dev/null and b/post/spieker_award/featured_hu34805b7071e74a97df17d3392ad52d03_143962_150x0_resize_q75_h2_lanczos.webp differ diff --git a/post/spieker_award/featured_hu34805b7071e74a97df17d3392ad52d03_143962_720x2500_fit_q75_h2_lanczos.webp b/post/spieker_award/featured_hu34805b7071e74a97df17d3392ad52d03_143962_720x2500_fit_q75_h2_lanczos.webp new file mode 100644 index 0000000..e4a9dc7 Binary files /dev/null and b/post/spieker_award/featured_hu34805b7071e74a97df17d3392ad52d03_143962_720x2500_fit_q75_h2_lanczos.webp differ diff --git a/post/spieker_award/index.html b/post/spieker_award/index.html new file mode 100644 index 0000000..bead2b3 --- /dev/null +++ b/post/spieker_award/index.html @@ -0,0 +1,1326 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Veronika Spieker wins the 1st place MedtecLIVE Talent Award 2022 | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Veronika Spieker wins the 1st place MedtecLIVE Talent Award 2022

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+ + + + + + + + + + + May 30, 2022 + + + + + + + + + 1 min read + + + + + + + + + + + +
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The MedtecLIVE Talent Award 2022 is given to bachelor’s and master’s theses that relate to an innovation, improvement, or new application in medical technology along with its entire value chain.

+

After a first screening of her thesis abstract, Veronika was invited to the live finale in Stuttgart to present her thesis in an 8-minute pitch. The extensiveness of her work, her drive to clinical translation as well as visual and interactive presentation convinced the jury to award her the first prize.

+

As part of her M.Sc. in Medical Technologies at TUM, Veronika conducted her master thesis at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and published her results in Sensors (www.mdpi.com/1424-8220/21/21/7404).

+

We are happy, that she is now pursuing her PhD at our lab at Helmholtz Munich!

+

More information on the finale can be found here:

+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
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My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Abstracts accepted at 2023 ISMRM & ISMRT Annual Meeting

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+ + + + + + + + + + + + + + + + Apr 25, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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Veronika Spieker’s and Hannah Eichhorn’s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM & ISMRT Annual Meeting.

+

Veronika Spieker will present her work on “Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction” on Monday, 05 June 2023 at 1:45 pm EDT.

+

Hannah Eichhorn will present her work on “Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations” on Tuesday, 06 June 2023 at 4:45 pm EDT. Check this GitHub repository for more information.

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Hannah Eichhorn
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PhD student
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Hannah Eichhorn’s research focuses on deep learning-based reconstruction of multi-parametric brain MRI.

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Veronika Spieker
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PhD Student
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Veronika Spieker’s interests include AI-based methods for MR reconstrution and motion correction.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Review paper accepted at IEEE Transactions on Medical Imaging

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+ + + + + + + + + + + + + + + + Oct 25, 2023 + + + + + + + + + 1 min read + + + + + + + + + + + +
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Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review by Veronika Spieker and Hannah Eichhorn et al. has been accepted for publication at IEEE Transactions on Medical Imaging.

+

Motion remains a major challenge in MRI and various deep learning solutions have been proposed – but what are common challenges and potentials? Check out this review, which identifies differences and synergies of recent methods and bridges the gap between AI and MR physics.

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+
Veronika Spieker
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PhD Student
+

Veronika Spieker’s interests include AI-based methods for MR reconstrution and motion correction.

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+ + + + + + + + + + + + +
+ + + Hannah Eichhorn + + +
+
Hannah Eichhorn
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PhD student
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Hannah Eichhorn’s research focuses on deep learning-based reconstruction of multi-parametric brain MRI.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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+ A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations +
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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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2

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+ Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review +
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+ Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, … +
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+ What do we learn? Debunking the Myth of Unsupervised Outlier Detection +
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What do we learn? Debunking the Myth of Unsupervised Outlier Detection

+ + + + + + +
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Abstract

+

Even though auto-encoders (AEs) have the desirable property of learning compact representations without labels and have been widely applied to out-of-distribution (OoD) detection, they are generally still poorly understood and are used incorrectly in detecting outliers where the normal and abnormal distributions are strongly overlapping. In general, the learned manifold is assumed to contain key information that is only important for describing samples within the training distribution, and that the reconstruction of outliers leads to high residual errors. However, recent work suggests that AEs are likely to be even better at reconstructing some types of OoD samples. In this work, we challenge this assumption and investigate what auto-encoders actually learn when they are posed to solve two different tasks. First, we propose two metrics based on the Fréchet inception distance (FID) and confidence scores of a trained classifier to assess whether AEs can learn the training distribution and reliably recognize samples from other domains. Second, we investigate whether AEs are able to synthesize normal images from samples with abnormal regions, on a more challenging lung pathology detection task. We have found that state-of-the-art (SOTA) AEs are either unable to constrain the latent manifold and allow reconstruction of abnormal patterns, or they are failing to accurately restore the inputs from their latent distribution, resulting in blurred or misaligned reconstructions. We propose novel deformable auto-encoders (MorphAEus) to learn perceptually aware global image priors and locally adapt their morphometry based on estimated dense deformation fields. We demonstrate superior performance over unsupervised methods in detecting OoD and pathology.

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Type
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+ + Preprint + +
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Publication
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In arxiv
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+ Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. +
+
+
+ + + + + + + +
+ + unsupervised outlier detection + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Cosmin I. Bercea + + +
+
Cosmin I. Bercea
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PhD Student
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My research interests include interpretable machine learning for anomaly detection.

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+ + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
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Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

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Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning

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Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
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My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information

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Medical Image Analysis
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Veronika Zimmer
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Principal Investigator
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My research focuses on image analysis and machine learning with a particular interest in robust and generalizable methods for multimodal registration and segmentation in medical imaging.

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Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
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My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

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arXiv preprint arXiv:2205.01673
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Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

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Abstract

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Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.

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Type
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Publication
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IEEE Transactions on Medical Imaging ( Early Access )
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+ + motion correction + + motion compensation + + magnetic resonance imaging + + deep learning + +
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+ + + Veronika Spieker + + +
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Veronika Spieker
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PhD Student
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Veronika Spieker’s interests include AI-based methods for MR reconstrution and motion correction.

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+ + + Hannah Eichhorn + + +
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Hannah Eichhorn
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PhD student
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Hannah Eichhorn’s research focuses on deep learning-based reconstruction of multi-parametric brain MRI.

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
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Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
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My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Cardiac MR motion artefacts

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+ Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning +
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+ Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical … +
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+

Convolutional neural networks

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+ Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning +
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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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deep learning

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+ Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review +
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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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Demons

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+ Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling +
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Discrete optimization

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+ Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tag/discrete-optimization/index.xml b/tag/discrete-optimization/index.xml new file mode 100644 index 0000000..7a30675 --- /dev/null +++ b/tag/discrete-optimization/index.xml @@ -0,0 +1,24 @@ + + + + Discrete optimization | Computational Imaging and AI in Medicine + https://compai-lab.io/tag/discrete-optimization/ + + Discrete optimization + Wowchemy (https://wowchemy.com)en-usSat, 01 Oct 2016 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + Discrete optimization + https://compai-lab.io/tag/discrete-optimization/ + + + + Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling + https://compai-lab.io/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/ + Sat, 01 Oct 2016 00:00:00 +0000 + https://compai-lab.io/fpublications/028-b-6-ad-81-dea-4-ce-39-a-182-f-7-df-77-f-2-ee-5/ + + + + + diff --git a/tag/discrete-optimization/page/1/index.html b/tag/discrete-optimization/page/1/index.html new file mode 100644 index 0000000..678df34 --- /dev/null +++ b/tag/discrete-optimization/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/tag/discrete-optimization/ + + + + + + diff --git a/tag/image-quality-assessment/index.html b/tag/image-quality-assessment/index.html new file mode 100644 index 0000000..977f834 --- /dev/null +++ b/tag/image-quality-assessment/index.html @@ -0,0 +1,1163 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Image quality assessment | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + +
+ +
+ + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + +
+

Image quality assessment

+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning +
+ + + +
+ Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical … +
+ + + +
+ + + + + + + +
+ + + + + + + + + + + + + + + + Jan 1, 2019 + + + + + + + + + + + + + + + +
+ + +
+ + +
+ + + + + + + + + + + + + + + + + PDF + + + + + + Cite + + + + + + + + + + + + + + + + DOI + + + + +
+ + +
+
+ + +
+
+ + + + + +
+
+ +
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tag/image-quality-assessment/index.xml b/tag/image-quality-assessment/index.xml new file mode 100644 index 0000000..8fc456d --- /dev/null +++ b/tag/image-quality-assessment/index.xml @@ -0,0 +1,24 @@ + + + + Image quality assessment | Computational Imaging and AI in Medicine + https://compai-lab.io/tag/image-quality-assessment/ + + Image quality assessment + Wowchemy (https://wowchemy.com)en-usTue, 01 Jan 2019 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + Image quality assessment + https://compai-lab.io/tag/image-quality-assessment/ + + + + Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning + https://compai-lab.io/fpublications/oksuz-2019136/ + Tue, 01 Jan 2019 00:00:00 +0000 + https://compai-lab.io/fpublications/oksuz-2019136/ + + + + + diff --git a/tag/image-quality-assessment/page/1/index.html b/tag/image-quality-assessment/page/1/index.html new file mode 100644 index 0000000..4b62d49 --- /dev/null +++ b/tag/image-quality-assessment/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/tag/image-quality-assessment/ + + + + + + diff --git a/tag/lstm/index.html b/tag/lstm/index.html new file mode 100644 index 0000000..5a05757 --- /dev/null +++ b/tag/lstm/index.html @@ -0,0 +1,1163 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + LSTM | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + +
+ +
+ + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + +
+

LSTM

+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning +
+ + + +
+ Good quality of medical images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical … +
+ + + +
+ + + + + + + +
+ + + + + + + + + + + + + + + + Jan 1, 2019 + + + + + + + + + + + + + + + +
+ + +
+ + +
+ + + + + + + + + + + + + + + + + PDF + + + + + + Cite + + + + + + + + + + + + + + + + DOI + + + + +
+ + +
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+
+ +
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tag/lstm/index.xml b/tag/lstm/index.xml new file mode 100644 index 0000000..7510e86 --- /dev/null +++ b/tag/lstm/index.xml @@ -0,0 +1,24 @@ + + + + LSTM | Computational Imaging and AI in Medicine + https://compai-lab.io/tag/lstm/ + + LSTM + Wowchemy (https://wowchemy.com)en-usTue, 01 Jan 2019 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + LSTM + https://compai-lab.io/tag/lstm/ + + + + Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning + https://compai-lab.io/fpublications/oksuz-2019136/ + Tue, 01 Jan 2019 00:00:00 +0000 + https://compai-lab.io/fpublications/oksuz-2019136/ + + + + + diff --git a/tag/lstm/page/1/index.html b/tag/lstm/page/1/index.html new file mode 100644 index 0000000..6599fa0 --- /dev/null +++ b/tag/lstm/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/tag/lstm/ + + + + + + diff --git a/tag/magnetic-resonance-imaging/index.html b/tag/magnetic-resonance-imaging/index.html new file mode 100644 index 0000000..3a6a763 --- /dev/null +++ b/tag/magnetic-resonance-imaging/index.html @@ -0,0 +1,1131 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + magnetic resonance imaging | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + +
+ +
+ + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + +
+

magnetic resonance imaging

+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review +
+ + + +
+ Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, … +
+ + + +
+ + + + + + +
+ + +
+ + + + + + + + + + + + + + + + + PDF + + + + + + Cite + + + + + + + + + + + + + + + + DOI + + + + +
+ + +
+
+ + + + + Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review + + +
+
+ + + + + +
+
+ +
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tag/magnetic-resonance-imaging/index.xml b/tag/magnetic-resonance-imaging/index.xml new file mode 100644 index 0000000..eea0608 --- /dev/null +++ b/tag/magnetic-resonance-imaging/index.xml @@ -0,0 +1,24 @@ + + + + magnetic resonance imaging | Computational Imaging and AI in Medicine + https://compai-lab.io/tag/magnetic-resonance-imaging/ + + magnetic resonance imaging + Wowchemy (https://wowchemy.com)en-usFri, 13 Oct 2023 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + magnetic resonance imaging + https://compai-lab.io/tag/magnetic-resonance-imaging/ + + + + Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + Fri, 13 Oct 2023 00:00:00 +0000 + https://compai-lab.io/publication/spiekereichhorn-2023-review/ + + + + + diff --git a/tag/magnetic-resonance-imaging/page/1/index.html b/tag/magnetic-resonance-imaging/page/1/index.html new file mode 100644 index 0000000..3d56eab --- /dev/null +++ b/tag/magnetic-resonance-imaging/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/tag/magnetic-resonance-imaging/ + + + + + + diff --git a/tag/master/index.html b/tag/master/index.html new file mode 100644 index 0000000..01fdb35 --- /dev/null +++ b/tag/master/index.html @@ -0,0 +1,1216 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + master | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + +
+ +
+ + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + +
+

master

+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ Exploring Riemannian Manifolds for Medical Image Classification (f/m/x) +
+ + + +
+ Master Thesis. I’m interested +
+ + + +
+ + + + + + + +
+ + + + + +
+ + + + Maxime Di Folco +
+ + + + + + + + + + Jul 8, 2023 + + + + + + + + + + + + + + + +
+ + +
+ + + +
+
+ + + + + Exploring Riemannian Manifolds for Medical Image Classification (f/m/x) + + +
+
+ + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x) +
+ + + +
+ Master Thesis. I’m interested +
+ + + +
+ + + + + + + +
+ + + + + +
+ + + + Veronika Zimmer +
+ + + + + + + + + + Nov 21, 2022 + + + + + + + + + + + + + + + +
+ + +
+ + + +
+
+ + + + + Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x) + + +
+
+ + + + + +
+
+ +
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tag/master/index.xml b/tag/master/index.xml new file mode 100644 index 0000000..bd1233c --- /dev/null +++ b/tag/master/index.xml @@ -0,0 +1,41 @@ + + + + master | Computational Imaging and AI in Medicine + https://compai-lab.io/tag/master/ + + master + Wowchemy (https://wowchemy.com)en-usSat, 08 Jul 2023 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + master + https://compai-lab.io/tag/master/ + + + + Exploring Riemannian Manifolds for Medical Image Classification (f/m/x) + https://compai-lab.io/vacancies/msc_manifold_anna/ + Sat, 08 Jul 2023 00:00:00 +0000 + https://compai-lab.io/vacancies/msc_manifold_anna/ + <p>Abstract:</p> +<p>This Master’s project aims to explore the use of covariance descriptors for disease classification with medical +images. First, the MedMNIST toy dataset will be explored. Then, the student will work with an open-source +medical dataset, e.g. of 2D chest x-ray or 3D cardiac MR images</p> + + + + + Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x) + https://compai-lab.io/vacancies/msc_surface/ + Mon, 21 Nov 2022 00:00:00 +0000 + https://compai-lab.io/vacancies/msc_surface/ + <p>Abstract:</p> +<p>In recent years, unsupervised and semi-supervised learning from populations of surfaces and curves has received a lot of attention. Such data representations are analyzed according to their shapes which open a broad range of applications in machine learning, robotics, statistics and engineering. In particular, studying the shape of surfaces have become an important tool in biology and medical imaging. The extraction of appropriate data representations, such as triangulated surfaces, is crucial for the subsequent analysis. These surfaces are for example obtained from binary segmentations or 3D point clouds. Using standard methods, such surfaces are often not very accurate and require several post-processing steps, such as smoothing and simplifications. +Deep learning based methods are of great interest in various fields such as medical imaging, com- puter vision, applied mathematics and are successfully used in the field of image segmentation. Gener- ally, a specific formulation requires a particular attention to representations, loss functions, probability models, optimization techniques, etc. This choice is very crucial due to the underlying geometry on the space of representations and constraints. we aim to develop a new set of automatic methods that can compute a triangulation and a normal field from a 3D dataset (binary image and/or 3D point cloud). +The goal of this project is to understand the-state-of-the-art methods (e.g., [?]) and to propose solutions in the context of constructing a mesh from 3D images/point sets. We are interested in learn- ing from a dataset of smooth surfaces and their corresponding 3D datasets to make the triangulation or resampling accurate. The application will be the extraction of a smooth surfaces from μ-CT and CT data of the cochlea and inner ear, whose shapes can then be analyzed subsequently for population studies. +To summarize, the key steps are : (i) Literature review and getting familiar with some state-of- the-art methods in the medical context; (ii) Implementing and testing the code before validation on real data; (iii) Optimizing the code and comparing with baseline methods. If successful, the method would be applied to analyze and classify surfaces.</p> + + + + + diff --git a/tag/master/page/1/index.html b/tag/master/page/1/index.html new file mode 100644 index 0000000..39583bc --- /dev/null +++ b/tag/master/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/tag/master/ + + + + + + diff --git a/tag/motion-compensation/index.html b/tag/motion-compensation/index.html new file mode 100644 index 0000000..b403be1 --- /dev/null +++ b/tag/motion-compensation/index.html @@ -0,0 +1,1131 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + motion compensation | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + +
+ +
+ + +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + +
+

motion compensation

+ + + + +
+ + + +
+ + + + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review +
+ + + +
+ Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, … +
+ + + +
+ + + + + + +
+ + +
+ + + + + + + + + + + + + + + + + PDF + + + + + + Cite + + + + + + + + + + + + + + + + DOI + + + + +
+ + +
+
+ + + + + Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review + + +
+
+ + + + + +
+
+ +
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
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+ +
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+ Medical Image Registation I (IN2107) +
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+ Summer semester 2022. TUM Informatics. Master Seminar. Details +
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+ Summer 2022. TUM Informatics. Lecture. Details. +
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+ + + + + + + + + + + Oct 1, 2021 + + + + + + + + + + + + + + + +
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+
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tag/summer/index.xml b/tag/summer/index.xml new file mode 100644 index 0000000..550292d --- /dev/null +++ b/tag/summer/index.xml @@ -0,0 +1,63 @@ + + + + summer | Computational Imaging and AI in Medicine + https://compai-lab.io/tag/summer/ + + summer + Wowchemy (https://wowchemy.com)en-usSat, 01 Jan 2022 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + summer + https://compai-lab.io/tag/summer/ + + + + Medical Image Registation I (IN2107) + https://compai-lab.io/teaching/master_seminar/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/teaching/master_seminar/ + <p><a href="https://campus.tum.de/tumonline/pl/ui/$ctx/wbLv.wbShowLVDetail?pStpSpNr=950627128" target="_blank" rel="noopener">Course details</a></p> +<p>Image registration is the process of aligning two or more images, and crucial for many image analysis pipelines. This seminar will cover selected material of image registration for medical imaging. Basic problem formulations to recent advances in the field will be discussed. This includes, but is not limited to:</p> +<ul> +<li>Learning and non-learning based image registration</li> +<li>Optimization techniques</li> +<li>Image registration for multi-modal data</li> +<li>Multi-resolution and regularization strategies</li> +<li>Linear and non-linear deformations</li> +<li>Supervised and unsupervised learning</li> +<li>Clinical applications</li> +</ul> + + + + + Artificial Intelligence in Medicine II (IN2408) + https://compai-lab.io/teaching/aim_lecture_2/ + Fri, 01 Oct 2021 00:00:00 +0000 + https://compai-lab.io/teaching/aim_lecture_2/ + <ul> +<li> +<p><a href="https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950636169&amp;pSpracheNr=2" target="_blank" rel="noopener">Course Details</a></p> +</li> +<li> +<p><a href="https://www.ph.tum.de/academics/org/cc/course/950636169/" target="_blank" rel="noopener">Basic Information</a></p> +</li> +<li> +<p>Content</p> +</li> +</ul> +<p>Introduction and examples of advanced prediction and classification problems in medicine; ML for prognostic and diagnostic tasks; risk scores, time-to-event modeling, survival models, differential diagnosis &amp; population stratification, geometric deep learning: point clouds &amp; meshes, mesh-based segmentation, shape analysis, trustworthy AI in medicine: bias and fairness, generalizability, AI for affordable healthcare, clinical deployment and evaluation, data harmonization, causal inference, transformers, reinforcement learning in medicine, ML for neuro: structural neuroimaging, functional neuroimaging, diffusion imaging, ML for CVD: EEG analysis</p> +<ul> +<li>Learning Outcome</li> +</ul> +<p>At the end of the module students should be able to recall advanced topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own AI projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.</p> +<ul> +<li>Preconditions</li> +</ul> +<p>IN2403 Artificial Intelligence in Medicine</p> + + + + + diff --git a/tag/summer/page/1/index.html b/tag/summer/page/1/index.html new file mode 100644 index 0000000..14a02b7 --- /dev/null +++ b/tag/summer/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/tag/summer/ + + + + + + diff --git a/tag/supervoxels/index.html b/tag/supervoxels/index.html new file mode 100644 index 0000000..f55ae7f --- /dev/null +++ b/tag/supervoxels/index.html @@ -0,0 +1,1150 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Supervoxels | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Supervoxels

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+ Advances and Challenges in Deformable Image Registration: From Image Fusion to Complex Motion Modelling +
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+ Over the past 20 years, the field of medical image registration has significantly advanced from multi-modal image fusion to highly … +
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unsupervised outlier detection

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+ What do we learn? Debunking the Myth of Unsupervised Outlier Detection +
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+ Even though auto-encoders (AEs) have the desirable property of learning compact representations without labels and have been widely … +
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winter

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+ Learning of and on manifolds in medical imaging (IN2107) +
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+ Winter semester 2023. TUM Informatics. Master Seminar. +
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+ + + + + + + + + + + Jul 19, 2023 + + + + + + + + + + + + + + + +
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+ Unsupervised Anomaly Detection in Medical Imaging +
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+ Winter semester 2023. TUM Informatics. Master Seminar. +
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+ + + + + + + + + + + Jul 19, 2023 + + + + + + + + + + + + + + + +
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+ Artificial Intelligence in Medicine (IN2403) +
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+ Winter 2021. TUM Informatics. Lecture. Details. +
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+ + + + + + + + + + + Oct 1, 2021 + + + + + + + + + + + + + + + +
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+ + + + + Artificial Intelligence in Medicine (IN2403) + + +
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+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/tag/winter/index.xml b/tag/winter/index.xml new file mode 100644 index 0000000..803ca57 --- /dev/null +++ b/tag/winter/index.xml @@ -0,0 +1,147 @@ + + + + winter | Computational Imaging and AI in Medicine + https://compai-lab.io/tag/winter/ + + winter + Wowchemy (https://wowchemy.com)en-usWed, 19 Jul 2023 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + winter + https://compai-lab.io/tag/winter/ + + + + Learning of and on manifolds in medical imaging (IN2107) + https://compai-lab.io/teaching/manifold_seminar/ + Wed, 19 Jul 2023 00:00:00 +0000 + https://compai-lab.io/teaching/manifold_seminar/ + <p><a href="https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950706204" target="_blank" rel="noopener">Course details</a></p> +<p>Considering the manifold of medical imaging data, i.e. the underlying topological space, facilitates the analysis, interpretation, and visualization of the data. This seminar focuses on machine and deep learning methods that either learn the manifold from high-dimensional data or use manifold-valued data as input. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed. This includes, but is not +limited to:</p> +<ul> +<li>Introduction to manifolds</li> +<li>Difference between learning on and of a manifold</li> +<li>Examples of manifold-valued data in medical imaging</li> +<li>State-of-the-art methods for manifold-valued data</li> +<li>Clinical applications</li> +</ul> +<p>Please register to: <a href="https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx" target="_blank" rel="noopener">https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx</a></p> +<p>Check the intro slides here: + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/files/Manifold_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<object data="/files/Manifold_seminar.pdf" type="application/pdf" width="100%" height="400"> +</object> + + + + + Unsupervised Anomaly Detection in Medical Imaging + https://compai-lab.io/teaching/anomaly_seminar/ + Wed, 19 Jul 2023 00:00:00 +0000 + https://compai-lab.io/teaching/anomaly_seminar/ + <p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/autoddpm_teaser.gif" alt="Teaser" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>Anomaly detection aims to identify patterns that do not conform to the expected normal distribution. Despite its importance for clinical applications, the detection of outliers is still a very challenging task due to the rarity, unknownness, diversity, and heterogeneity of anomalies. Basic problem formulations to recent advances in the field will be discussed.</p> +<p>This includes, but is not limited to:</p> +<ul> +<li>Reconstruction-based anomaly segmentation</li> +<li>Probabilistic models, i.e., anomaly likelihood estimation</li> +<li>Generative models</li> +<li>Self-supervised-, contrastive methods</li> +<li>Unsupervised methods</li> +<li>Clinical Applications</li> +</ul> +<p>Please register via the TUM matching system: <a href="https://matching.in.tum.de" target="_blank" rel="noopener">https://matching.in.tum.de</a></p> +<p>Check the intro slides here: + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/files/UAD_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<object data="/files/UAD_seminar.pdf" type="application/pdf" width="100%" height="400"> +</object> + + + + + Artificial Intelligence in Medicine (IN2403) + https://compai-lab.io/teaching/aim_lecture/ + Fri, 01 Oct 2021 00:00:00 +0000 + https://compai-lab.io/teaching/aim_lecture/ + <ul> +<li><a href="https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950596772" target="_blank" rel="noopener">Course Details</a></li> +<li><a href="https://www.ph.tum.de/academics/org/cc/mh/IN2403/" target="_blank" rel="noopener">Basic Information</a></li> +</ul> +<p>At the end of the module students should be able to recall the important topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own deep learning projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.</p> +<ul> +<li>Introduction: Clinical motivation, clinical data, clinical workflows</li> +<li>ML for medical imaging• Data curation for medical applications</li> +<li>Domain shift in medical applications: Adversarial learning and Transfer learning</li> +<li>Self-supervised learning and unsupervised learning</li> +<li>Learning from sparse and noisy data</li> +<li>ML for unstructured and multi-modal clinical data</li> +<li>NLP for clinical data• Bayesian approaches to deep learning and uncertainty</li> +<li>Interpretability and explainability</li> +<li>Federated learning, privacy-preserving ML and ethics</li> +<li>ML for time-to-event modeling, survival models</li> +<li>ML for differential diagnosis and stratification• Clinical applications in pathology/radiology/omics</li> +</ul> + + + + + diff --git a/tag/winter/page/1/index.html b/tag/winter/page/1/index.html new file mode 100644 index 0000000..4032342 --- /dev/null +++ b/tag/winter/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://compai-lab.io/tag/winter/ + + + + + + diff --git a/tags/index.html b/tags/index.html new file mode 100644 index 0000000..e00786a --- /dev/null +++ b/tags/index.html @@ -0,0 +1,1763 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Tags | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Artificial Intelligence in Medicine (IN2403)

+ + + + + + +
+ + + + + + + + + + + Oct 1, 2021 + + + + + + + + + + + + + + + +
+ + + + + + + +
+ + +
+
+ + +
+
+ + + +
+ +
+ +

At the end of the module students should be able to recall the important topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own deep learning projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.

+
    +
  • Introduction: Clinical motivation, clinical data, clinical workflows
    • +
  • ML for medical imaging• Data curation for medical applications
    • +
  • Domain shift in medical applications: Adversarial learning and Transfer learning
    • +
  • Self-supervised learning and unsupervised learning
    • +
  • Learning from sparse and noisy data
    • +
  • ML for unstructured and multi-modal clinical data
    • +
  • NLP for clinical data• Bayesian approaches to deep learning and uncertainty
    • +
  • Interpretability and explainability
    • +
  • Federated learning, privacy-preserving ML and ethics
    • +
  • ML for time-to-event modeling, survival models
    • +
  • ML for differential diagnosis and stratification• Clinical applications in pathology/radiology/omics
    • +
+ +
+ + + + + + + +
+ + winter + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

+ + +
+
+ + + + + + + + + + + + + + + + + + +
+
+
+ +
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
+ +
+ +
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+ +
+ + +
+ +
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
+

Artificial Intelligence in Medicine II (IN2408)

+ + + + + + +
+ + + + + + + + + + + Oct 1, 2021 + + + + + + + + + + + + + + + +
+ + + + + + + +
+ + +
+
+ + +
+
+ + + +
+ +
+ +

Introduction and examples of advanced prediction and classification problems in medicine; ML for prognostic and diagnostic tasks; risk scores, time-to-event modeling, survival models, differential diagnosis & population stratification, geometric deep learning: point clouds & meshes, mesh-based segmentation, shape analysis, trustworthy AI in medicine: bias and fairness, generalizability, AI for affordable healthcare, clinical deployment and evaluation, data harmonization, causal inference, transformers, reinforcement learning in medicine, ML for neuro: structural neuroimaging, functional neuroimaging, diffusion imaging, ML for CVD: EEG analysis

+
    +
  • Learning Outcome
    • +
+

At the end of the module students should be able to recall advanced topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own AI projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.

+
    +
  • Preconditions
    • +
+

IN2403 Artificial Intelligence in Medicine

+ +
+ + + + + + + +
+ + summer + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

+ + +
+
+ + + + + + + + + + + + + + + + + + +
+
+
+ +
+ + +
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
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+

Unsupervised Anomaly Detection in Medical Imaging

+ + + + + + +
+ + + + + + + + + + + Jul 19, 2023 + + + + + + + + + + + + + + + +
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Teaser
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+

+

Anomaly detection aims to identify patterns that do not conform to the expected normal distribution. Despite its importance for clinical applications, the detection of outliers is still a very challenging task due to the rarity, unknownness, diversity, and heterogeneity of anomalies. Basic problem formulations to recent advances in the field will be discussed.

+

This includes, but is not limited to:

+
    +
  • Reconstruction-based anomaly segmentation
    • +
  • Probabilistic models, i.e., anomaly likelihood estimation
    • +
  • Generative models
    • +
  • Self-supervised-, contrastive methods
    • +
  • Unsupervised methods
    • +
  • Clinical Applications
    • +
+

Please register via the TUM matching system: https://matching.in.tum.de

+

Check the intro slides here: + + + + + + + + + + + + + + + +

+
+
Slides
+
+

+ + + +
+ + + + + + + +
+ + winter + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
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Open Positions

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+ + + + + + + + + + + + + + + + + + + + + +
+
+ +
+ Learning of and on manifolds in medical imaging (IN2107) +
+ + + +
+ Winter semester 2023. TUM Informatics. Master Seminar. +
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+ + + + + + + +
+ + + + + + + + + + + Jul 19, 2023 + + + + + + + + + + + + + + + +
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+ + + +
+
+ + + + + Learning of and on manifolds in medical imaging (IN2107) + + +
+
+ + + + + + + + + + + + + + + + + + + +
+
+ +
+ Unsupervised Anomaly Detection in Medical Imaging +
+ + + +
+ Winter semester 2023. TUM Informatics. Master Seminar. +
+ + + +
+ + + + + + + +
+ + + + + + + + + + + Jul 19, 2023 + + + + + + + + + + + + + + + +
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+ + + +
+
+ + + + + Unsupervised Anomaly Detection in Medical Imaging + + +
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+ + + + + + + + + + + + + + + + + + + +
+
+ +
+ Medical Image Registation I (IN2107) +
+ + + +
+ Summer semester 2022. TUM Informatics. Master Seminar. Details +
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+ + + + + + + +
+ + + + + + + + + + + Jan 1, 2022 + + + + + + + + + + + + + + + +
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+ + + +
+
+ + + + + Medical Image Registation I (IN2107) + + +
+
+ + + + + + + + + + + + + + + + + + + +
+
+ +
+ Artificial Intelligence in Medicine (IN2403) +
+ + + +
+ Winter 2021. TUM Informatics. Lecture. Details. +
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+ + + + + + + +
+ + + + + + + + + + + Oct 1, 2021 + + + + + + + + + + + + + + + +
+ + +
+ + + +
+
+ + + + + Artificial Intelligence in Medicine (IN2403) + + +
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+ + + + + + + + + + + + + + + + + + + +
+
+ +
+ Artificial Intelligence in Medicine II (IN2408) +
+ + + +
+ Summer 2022. TUM Informatics. Lecture. Details. +
+ + + +
+ + + + + + + +
+ + + + + + + + + + + Oct 1, 2021 + + + + + + + + + + + + + + + +
+ + +
+ + + +
+
+ + + + + Artificial Intelligence in Medicine II (IN2408) + + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/teaching/index.xml b/teaching/index.xml new file mode 100644 index 0000000..b61f7b7 --- /dev/null +++ b/teaching/index.xml @@ -0,0 +1,194 @@ + + + + Open Positions | Computational Imaging and AI in Medicine + https://compai-lab.io/teaching/ + + Open Positions + Wowchemy (https://wowchemy.com)en-usWed, 19 Jul 2023 00:00:00 +0000 + + https://compai-lab.io/media/icon_hu790efcb2e4090d1e7a0ffec0a0776e8f_331139_512x512_fill_lanczos_center_3.png + Open Positions + https://compai-lab.io/teaching/ + + + + Learning of and on manifolds in medical imaging (IN2107) + https://compai-lab.io/teaching/manifold_seminar/ + Wed, 19 Jul 2023 00:00:00 +0000 + https://compai-lab.io/teaching/manifold_seminar/ + <p><a href="https://campus.tum.de/tumonline/wblv.wbShowLvDetail?pStpSpNr=950706204" target="_blank" rel="noopener">Course details</a></p> +<p>Considering the manifold of medical imaging data, i.e. the underlying topological space, facilitates the analysis, interpretation, and visualization of the data. This seminar focuses on machine and deep learning methods that either learn the manifold from high-dimensional data or use manifold-valued data as input. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed. This includes, but is not +limited to:</p> +<ul> +<li>Introduction to manifolds</li> +<li>Difference between learning on and of a manifold</li> +<li>Examples of manifold-valued data in medical imaging</li> +<li>State-of-the-art methods for manifold-valued data</li> +<li>Clinical applications</li> +</ul> +<p>Please register to: <a href="https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx" target="_blank" rel="noopener">https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx</a></p> +<p>Check the intro slides here: + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/files/Manifold_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<object data="/files/Manifold_seminar.pdf" type="application/pdf" width="100%" height="400"> +</object> + + + + + Unsupervised Anomaly Detection in Medical Imaging + https://compai-lab.io/teaching/anomaly_seminar/ + Wed, 19 Jul 2023 00:00:00 +0000 + https://compai-lab.io/teaching/anomaly_seminar/ + <p> + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/images/autoddpm_teaser.gif" alt="Teaser" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<p>Anomaly detection aims to identify patterns that do not conform to the expected normal distribution. Despite its importance for clinical applications, the detection of outliers is still a very challenging task due to the rarity, unknownness, diversity, and heterogeneity of anomalies. Basic problem formulations to recent advances in the field will be discussed.</p> +<p>This includes, but is not limited to:</p> +<ul> +<li>Reconstruction-based anomaly segmentation</li> +<li>Probabilistic models, i.e., anomaly likelihood estimation</li> +<li>Generative models</li> +<li>Self-supervised-, contrastive methods</li> +<li>Unsupervised methods</li> +<li>Clinical Applications</li> +</ul> +<p>Please register via the TUM matching system: <a href="https://matching.in.tum.de" target="_blank" rel="noopener">https://matching.in.tum.de</a></p> +<p>Check the intro slides here: + + + + + + + + + + + + + + + +<figure > + <div class="d-flex justify-content-center"> + <div class="w-100" ><img src="https://compai-lab.io/files/UAD_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div> + </div></figure> +</p> +<object data="/files/UAD_seminar.pdf" type="application/pdf" width="100%" height="400"> +</object> + + + + + Medical Image Registation I (IN2107) + https://compai-lab.io/teaching/master_seminar/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/teaching/master_seminar/ + <p><a href="https://campus.tum.de/tumonline/pl/ui/$ctx/wbLv.wbShowLVDetail?pStpSpNr=950627128" target="_blank" rel="noopener">Course details</a></p> +<p>Image registration is the process of aligning two or more images, and crucial for many image analysis pipelines. This seminar will cover selected material of image registration for medical imaging. Basic problem formulations to recent advances in the field will be discussed. This includes, but is not limited to:</p> +<ul> +<li>Learning and non-learning based image registration</li> +<li>Optimization techniques</li> +<li>Image registration for multi-modal data</li> +<li>Multi-resolution and regularization strategies</li> +<li>Linear and non-linear deformations</li> +<li>Supervised and unsupervised learning</li> +<li>Clinical applications</li> +</ul> + + + + + Artificial Intelligence in Medicine (IN2403) + https://compai-lab.io/teaching/aim_lecture/ + Fri, 01 Oct 2021 00:00:00 +0000 + https://compai-lab.io/teaching/aim_lecture/ + <ul> +<li><a href="https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950596772" target="_blank" rel="noopener">Course Details</a></li> +<li><a href="https://www.ph.tum.de/academics/org/cc/mh/IN2403/" target="_blank" rel="noopener">Basic Information</a></li> +</ul> +<p>At the end of the module students should be able to recall the important topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own deep learning projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.</p> +<ul> +<li>Introduction: Clinical motivation, clinical data, clinical workflows</li> +<li>ML for medical imaging• Data curation for medical applications</li> +<li>Domain shift in medical applications: Adversarial learning and Transfer learning</li> +<li>Self-supervised learning and unsupervised learning</li> +<li>Learning from sparse and noisy data</li> +<li>ML for unstructured and multi-modal clinical data</li> +<li>NLP for clinical data• Bayesian approaches to deep learning and uncertainty</li> +<li>Interpretability and explainability</li> +<li>Federated learning, privacy-preserving ML and ethics</li> +<li>ML for time-to-event modeling, survival models</li> +<li>ML for differential diagnosis and stratification• Clinical applications in pathology/radiology/omics</li> +</ul> + + + + + Artificial Intelligence in Medicine II (IN2408) + https://compai-lab.io/teaching/aim_lecture_2/ + Fri, 01 Oct 2021 00:00:00 +0000 + https://compai-lab.io/teaching/aim_lecture_2/ + <ul> +<li> +<p><a href="https://campus.tum.de/tumonline/wbLv.wbShowLVDetail?pStpSpNr=950636169&amp;pSpracheNr=2" target="_blank" rel="noopener">Course Details</a></p> +</li> +<li> +<p><a href="https://www.ph.tum.de/academics/org/cc/course/950636169/" target="_blank" rel="noopener">Basic Information</a></p> +</li> +<li> +<p>Content</p> +</li> +</ul> +<p>Introduction and examples of advanced prediction and classification problems in medicine; ML for prognostic and diagnostic tasks; risk scores, time-to-event modeling, survival models, differential diagnosis &amp; population stratification, geometric deep learning: point clouds &amp; meshes, mesh-based segmentation, shape analysis, trustworthy AI in medicine: bias and fairness, generalizability, AI for affordable healthcare, clinical deployment and evaluation, data harmonization, causal inference, transformers, reinforcement learning in medicine, ML for neuro: structural neuroimaging, functional neuroimaging, diffusion imaging, ML for CVD: EEG analysis</p> +<ul> +<li>Learning Outcome</li> +</ul> +<p>At the end of the module students should be able to recall advanced topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own AI projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.</p> +<ul> +<li>Preconditions</li> +</ul> +<p>IN2403 Artificial Intelligence in Medicine</p> + + + + + diff --git a/teaching/manifold_seminar/featured.jpg b/teaching/manifold_seminar/featured.jpg new file mode 100644 index 0000000..970bbbd Binary files /dev/null and b/teaching/manifold_seminar/featured.jpg differ diff --git a/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_150x0_resize_q75_h2_lanczos.webp b/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_150x0_resize_q75_h2_lanczos.webp new file mode 100644 index 0000000..266fa8f Binary files /dev/null and b/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_150x0_resize_q75_h2_lanczos.webp differ diff --git a/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_550x0_resize_q75_h2_lanczos.webp b/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_550x0_resize_q75_h2_lanczos.webp new file mode 100644 index 0000000..d0e6a69 Binary files /dev/null and b/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_550x0_resize_q75_h2_lanczos.webp differ diff --git a/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_720x2500_fit_q75_h2_lanczos.webp b/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_720x2500_fit_q75_h2_lanczos.webp new file mode 100644 index 0000000..fee7a3d Binary files /dev/null and b/teaching/manifold_seminar/featured_hub43fcfa0e744b71d6201004a77134d9b_396543_720x2500_fit_q75_h2_lanczos.webp differ diff --git a/teaching/manifold_seminar/index.html b/teaching/manifold_seminar/index.html new file mode 100644 index 0000000..e972583 --- /dev/null +++ b/teaching/manifold_seminar/index.html @@ -0,0 +1,1302 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Learning of and on manifolds in medical imaging (IN2107) | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Learning of and on manifolds in medical imaging (IN2107)

+ + + + + + +
+ + + + + + + + + + + Jul 19, 2023 + + + + + + + + + + + + + + + +
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Course details

+

Considering the manifold of medical imaging data, i.e. the underlying topological space, facilitates the analysis, interpretation, and visualization of the data. This seminar focuses on machine and deep learning methods that either learn the manifold from high-dimensional data or use manifold-valued data as input. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed. This includes, but is not +limited to:

+
    +
  • Introduction to manifolds
    • +
  • Difference between learning on and of a manifold
    • +
  • Examples of manifold-valued data in medical imaging
    • +
  • State-of-the-art methods for manifold-valued data
    • +
  • Clinical applications
    • +
+

Please register to: https://matching.in.tum.de/m/jz0zflh/q/6wi1lmq4yx

+

Check the intro slides here: + + + + + + + + + + + + + + + +

+
+
Slides
+
+

+ + + +
+ + + + + + + +
+ + winter + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + Julia A. Schnabel + + +
+
Julia A. Schnabel
+
Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
+

My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

+ + +
+
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+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

+
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Medical Image Registation I (IN2107)

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Course details

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Image registration is the process of aligning two or more images, and crucial for many image analysis pipelines. This seminar will cover selected material of image registration for medical imaging. Basic problem formulations to recent advances in the field will be discussed. This includes, but is not limited to:

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  • Learning and non-learning based image registration
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  • Optimization techniques
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  • Image registration for multi-modal data
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  • Multi-resolution and regularization strategies
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  • Linear and non-linear deformations
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  • Supervised and unsupervised learning
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  • Clinical applications
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+ + summer + +
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+ + + Julia A. Schnabel + + +
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Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
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My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ 👋 Welcome to the group +

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+ Take a look at what we’re working on… +

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+ Lunch & Learn ☕️ +

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+ Share your knowledge with the group and explore exciting new topics together! +

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+ World-Class Semiconductor Lab +

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+ Just opened last month! +

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+ Join Us +

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Open Positions

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+ Exploring Riemannian Manifolds for Medical Image Classification (f/m/x) +
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+ Master Thesis. I’m interested +
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+ + + + Maxime Di Folco +
+ + + + + + + + + + Jul 8, 2023 + + + + + + + + + + + + + + + +
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+ Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x) +
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+ Master Thesis. I’m interested +
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+ + + + Veronika Zimmer +
+ + + + + + + + + + Nov 21, 2022 + + + + + + + + + + + + + + + +
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+ + + + + Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x) + + +
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+ PhD AI-enabled medical imaging (f/m/x) +
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+ PhD position starting July 2022. Position related to MCML Munich. I’m interested +
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+ + + + + + + + + + + Jan 1, 2022 + + + + + + + + + + + + + + + +
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+ + + + + PhD AI-enabled medical imaging (f/m/x) + + +
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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

+ + +

+ + + + + + + + + + +

+ + + + +

+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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First, the MedMNIST toy dataset will be explored. Then, the student will work with an open-source +medical dataset, e.g. of 2D chest x-ray or 3D cardiac MR images</p> + + + + + Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x) + https://compai-lab.io/vacancies/msc_surface/ + Mon, 21 Nov 2022 00:00:00 +0000 + https://compai-lab.io/vacancies/msc_surface/ + <p>Abstract:</p> +<p>In recent years, unsupervised and semi-supervised learning from populations of surfaces and curves has received a lot of attention. Such data representations are analyzed according to their shapes which open a broad range of applications in machine learning, robotics, statistics and engineering. In particular, studying the shape of surfaces have become an important tool in biology and medical imaging. The extraction of appropriate data representations, such as triangulated surfaces, is crucial for the subsequent analysis. These surfaces are for example obtained from binary segmentations or 3D point clouds. Using standard methods, such surfaces are often not very accurate and require several post-processing steps, such as smoothing and simplifications. +Deep learning based methods are of great interest in various fields such as medical imaging, com- puter vision, applied mathematics and are successfully used in the field of image segmentation. Gener- ally, a specific formulation requires a particular attention to representations, loss functions, probability models, optimization techniques, etc. This choice is very crucial due to the underlying geometry on the space of representations and constraints. we aim to develop a new set of automatic methods that can compute a triangulation and a normal field from a 3D dataset (binary image and/or 3D point cloud). +The goal of this project is to understand the-state-of-the-art methods (e.g., [?]) and to propose solutions in the context of constructing a mesh from 3D images/point sets. We are interested in learn- ing from a dataset of smooth surfaces and their corresponding 3D datasets to make the triangulation or resampling accurate. The application will be the extraction of a smooth surfaces from μ-CT and CT data of the cochlea and inner ear, whose shapes can then be analyzed subsequently for population studies. +To summarize, the key steps are : (i) Literature review and getting familiar with some state-of- the-art methods in the medical context; (ii) Implementing and testing the code before validation on real data; (iii) Optimizing the code and comparing with baseline methods. If successful, the method would be applied to analyze and classify surfaces.</p> + + + + + PhD AI-enabled medical imaging (f/m/x) + https://compai-lab.io/vacancies/phd-job/ + Sat, 01 Jan 2022 00:00:00 +0000 + https://compai-lab.io/vacancies/phd-job/ + + + + + diff --git a/vacancies/msc_manifold_anna/featured.jpg b/vacancies/msc_manifold_anna/featured.jpg new file mode 100644 index 0000000..8916615 Binary files /dev/null and b/vacancies/msc_manifold_anna/featured.jpg differ diff --git a/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_150x0_resize_q75_h2_lanczos.webp b/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_150x0_resize_q75_h2_lanczos.webp new file mode 100644 index 0000000..7c488bb Binary files /dev/null and b/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_150x0_resize_q75_h2_lanczos.webp differ diff --git a/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_550x0_resize_q75_h2_lanczos.webp b/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_550x0_resize_q75_h2_lanczos.webp new file mode 100644 index 0000000..cf4e29f Binary files /dev/null and b/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_550x0_resize_q75_h2_lanczos.webp differ diff --git a/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_720x2500_fit_q75_h2_lanczos.webp b/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_720x2500_fit_q75_h2_lanczos.webp new file mode 100644 index 0000000..9018eb3 Binary files /dev/null and b/vacancies/msc_manifold_anna/featured_hu64fb616fd589b310a1ffa19dd93eee83_5121856_720x2500_fit_q75_h2_lanczos.webp differ diff --git a/vacancies/msc_manifold_anna/index.html b/vacancies/msc_manifold_anna/index.html new file mode 100644 index 0000000..913d93a --- /dev/null +++ b/vacancies/msc_manifold_anna/index.html @@ -0,0 +1,1321 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + Exploring Riemannian Manifolds for Medical Image Classification (f/m/x) | Computational Imaging and AI in Medicine + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Exploring Riemannian Manifolds for Medical Image Classification (f/m/x)

+ + + + + + +
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+ + + + Maxime Di Folco +
+ + + + + + + + + + Jul 8, 2023 + + + + + + + + + + + + + + + +
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+ + Go to Project Site + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Abstract:

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This Master’s project aims to explore the use of covariance descriptors for disease classification with medical +images. First, the MedMNIST toy dataset will be explored. Then, the student will work with an open-source +medical dataset, e.g. of 2D chest x-ray or 3D cardiac MR images

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+ + + Maxime Di Folco + + +
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Maxime Di Folco
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Research Scientist
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My research interest is the study of the cardiac function via machine learning methods, in particular representation learning methods that aim to acquire low dimensional representation of high dimensional data. I have a strong interest in cardiac remodelling (adaptation of the heart to its environment or a disease), notably the study of the deformation and shape aspects.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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Deep Learning for Smooth Surface and Normal Fields Reconstruction (f/m/x)

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Abstract:

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In recent years, unsupervised and semi-supervised learning from populations of surfaces and curves has received a lot of attention. Such data representations are analyzed according to their shapes which open a broad range of applications in machine learning, robotics, statistics and engineering. In particular, studying the shape of surfaces have become an important tool in biology and medical imaging. The extraction of appropriate data representations, such as triangulated surfaces, is crucial for the subsequent analysis. These surfaces are for example obtained from binary segmentations or 3D point clouds. Using standard methods, such surfaces are often not very accurate and require several post-processing steps, such as smoothing and simplifications. +Deep learning based methods are of great interest in various fields such as medical imaging, com- puter vision, applied mathematics and are successfully used in the field of image segmentation. Gener- ally, a specific formulation requires a particular attention to representations, loss functions, probability models, optimization techniques, etc. This choice is very crucial due to the underlying geometry on the space of representations and constraints. we aim to develop a new set of automatic methods that can compute a triangulation and a normal field from a 3D dataset (binary image and/or 3D point cloud). +The goal of this project is to understand the-state-of-the-art methods (e.g., [?]) and to propose solutions in the context of constructing a mesh from 3D images/point sets. We are interested in learn- ing from a dataset of smooth surfaces and their corresponding 3D datasets to make the triangulation or resampling accurate. The application will be the extraction of a smooth surfaces from μ-CT and CT data of the cochlea and inner ear, whose shapes can then be analyzed subsequently for population studies. +To summarize, the key steps are : (i) Literature review and getting familiar with some state-of- the-art methods in the medical context; (ii) Implementing and testing the code before validation on real data; (iii) Optimizing the code and comparing with baseline methods. If successful, the method would be applied to analyze and classify surfaces.

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Veronika Zimmer
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Principal Investigator
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My research focuses on image analysis and machine learning with a particular interest in robust and generalizable methods for multimodal registration and segmentation in medical imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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PhD AI-enabled medical imaging (f/m/x)

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Julia A. Schnabel
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Professor for Computational Imaging and AI in Medicine, Director of the Institute of Machine Learning in Biomedical Imaging
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My research interests include machine/deep learning, nonlinear motion modeling, as well as multimodal and quantitative imaging, for cancer-, cardiac-, neuro- and perinatal imaging.

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+ © 2024 Me. This work is licensed under CC BY NC ND 4.0 +

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+ + + + + + + + + + Published with Wowchemy — the free, open source website builder that empowers creators. + +

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