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 current research interests lie in data-driven models for image registration, physics-inspired regularization, 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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Teaching
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Medical Image Registration
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Master seminar | WS24/25 |
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Learning of and on Manifolds in Medical Imaging
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Master seminar | WS23/24 |
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Student Projects & Theses
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Exploring SPD Feature Descriptors for Medical Image Classification
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+ Anna Reithmeir | Computational Imaging and AI in Medicine
+ https://compai-lab.io/author/anna-reithmeir/
+
+ Anna Reithmeir
+ Wowchemy (https://wowchemy.com)en-usThu, 25 Jul 2024 00:00:00 +0000
+
+ https://compai-lab.io/author/anna-reithmeir/avatar_hua2d592eef98e1fa5362a62d72665a4f3_289115_270x270_fill_q75_lanczos_center.jpg
+ Anna Reithmeir
+ https://compai-lab.io/author/anna-reithmeir/
+
+
+
+ Master Seminar - Medical Image Registration (IN2107, IN4462)
+ https://compai-lab.io/old_stuff/teaching/registration_seminar_ws24/
+ Thu, 25 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/old_stuff/teaching/registration_seminar_ws24/
+ <p><strong>Time</strong>: Wednesday 10-12 a.m.</p>
+<p><strong>Location</strong>: Garching (in-person)</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>
+<p>Requirements:</p>
+<ul>
+<li>Background in image processing and machine learning</li>
+<li>Interest in medical image analysis</li>
+</ul>
+<p>Goal and organization:</p>
+<p>The participating students will learn the fundamental concepts of image registration. They will acquire the skills to analyze critically state-of-the-art research work and to define own research questions. Basic concepts will be introduced with an overview of different research topics.
+The participants will select a research paper (suggestions given by the lecturers) and independently work on it with a final oral presentation and a written report.
+Presentations of members of international research groups will provide the students with insights into state-of-the-art research in the field.</p>
+<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> or write an email to <a href="mailto:anna.reithmeir@tum.de">anna.reithmeir@tum.de</a>.</p>
+<p>The seminar will take place Wednesdays from 10 a.m. to 12.a.m. in Garching.</p>
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+ Eleven papers accepted at MICCAI Workshops 2024
+ https://compai-lab.io/post/miccai_workshops_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_workshops_24/
+ <ul>
+<li>
+<p><strong>Selective Test-Time Adaptation using Neural Implicit Representations for Unsupervised Anomaly Detection [Best Paper Award]</strong><br>
+Sameer Ambekar, Julia Schnabel, and Cosmin I. Bercea. <br>
+<a href="https://arxiv.org/abs/2410.03306" target="_blank" rel="noopener">https://arxiv.org/abs/2410.03306</a><br/><br/></p>
+</li>
+<li>
+<p><strong>MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI</strong><br>
+Malek Ben Alaya, Daniel M. Lang, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15270" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15270</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders</strong><br>
+Mehmet Yigit Avci, Emily Chan, Veronika Zimmer, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.03863" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.03863</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models</strong><br>
+Deniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco<br>
+(<a href="https://arxiv.org/abs/2407.16405" target="_blank" rel="noopener">https://arxiv.org/abs/2407.16405</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Graph Neural Networks: A suitable alternative to MLPs in latent 3D medical image classification?</strong><br>
+Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer, Lina Felsner, Jan C. Peeken, Julia A. Schnabel<br>
+(<a href="http://arxiv.org/abs/2407.17219" target="_blank" rel="noopener">http://arxiv.org/abs/2407.17219</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>General Vision Encoder Features as Guidance in Medical Image Registration</strong><br>
+Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa, Ines Machado, Rickmer Braren, Daniel Rückert, Julia A Schnabel, Veronika A Zimmer<br>
+(<a href="https://arxiv.org/abs/2407.13311" target="_blank" rel="noopener">https://arxiv.org/abs/2407.13311</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Language Models Meet Anomaly Detection for Better Interpretability and Generalizability</strong><br>
+Jun Li, Su Hwan Kim, Philip Müller, Lina Felsner, Daniel Rueckert, Benedikt Wiestler, Julia A.Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2404.07622v2" target="_blank" rel="noopener">https://arxiv.org/pdf/2404.07622v2</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer</strong><br>
+Inês P. Machado, Anna Reithmeir, Fryderyk Kogl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James Brenton, Julia Schnabel, Mireia Crispin<br>
+(<a href="https://arxiv.org/abs/2407.17114" target="_blank" rel="noopener">https://arxiv.org/abs/2407.17114</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound</strong><br>
+Hanna Mykula, Lisa Gasser, Silvia Lobmaier, Julia A. Schnabel, Veronika Zimmer, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15119" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15119</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data</strong><br>
+Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2407.12669" target="_blank" rel="noopener">https://arxiv.org/abs/2407.12669</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Complex-valued Federated Learning with Differential Privacy and MRI Applications</strong><br>
+Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia Schnabel, Georgios Kaissis <br>
+([link will be available soon])<br/><br/></p>
+</li>
+</ul>
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+ Seven papers accepted at MICCAI 2024
+ https://compai-lab.io/post/miccai_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_24/
+ <ul>
+<li>
+<p><strong>Diffusion Models with Implicit Guidance for Medical Anomaly Detection</strong><br>
+Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08464" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08464</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI</strong><br>
+Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08298" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08298</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks</strong><br>
+Stefan M. Fischer, Lina Felsner, Daniel M. Lang, Richard Osuala, Johannes Kiechle, Jan C. Peeken, Julia A. Schnabel<br/><br/></p>
+</li>
+<li>
+<p><strong>Interpretable Representation Learning of Cardiac MRI via Attribute Regularization</strong><br>
+Maxime Di Folco, Cosmin I. Bercea, Emily Chan, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2406.08282" target="_blank" rel="noopener">https://arxiv.org/abs/2406.08282</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models</strong><br>
+Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia Schnabel, and Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2403.13890" target="_blank" rel="noopener">https://arxiv.org/abs/2403.13890</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration</strong><br>
+Anna Reithmeir, Lina Felsner, Rickmer Braren, Julia A. Schnabel, Veronika A. Zimmer<br/><br/></p>
+</li>
+<li>
+<p><strong>Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation</strong><br>
+Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rückert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2404.08350" target="_blank" rel="noopener">https://arxiv.org/abs/2404.08350</a>)<br/><br/></p>
+</li>
+</ul>
+
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+
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+ Paper accepted at SPIE Medical Imaging 2024 and Finalist of Best Student Paper Award
+ https://compai-lab.io/post/reithmeir_spie_24/
+ Wed, 20 Mar 2024 00:00:00 +0000
+ https://compai-lab.io/post/reithmeir_spie_24/
+ <p>Anna Reithmeir’s paper ‘Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks’ was accepted at SPIE Medical Imaging 2024 which was held 18-22 Feb. 2024 in San Diego, US.</p>
+<p>The paper is among the finalists for the best student paper award.</p>
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+ Anneliese Riess | Computational Imaging and AI in Medicine
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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.
Chun Kit Wong is a Ph.D. student at the Technical University of Denmark (SONAI project group), working on translating AI to fetal ultrasound clinic. Prior to this he studied liver diseases with histology images in the industry. Even earlier than that he was with an academic lab in Singapore, where he worked on MRI image analysis and sequence programming, in addition to providing research computing support to the lab.
Cosmin Bercea is a postdoctoral researcher at the Computational Imaging and AI in Medicine chair (Prof. Schnabel), TUM School of Computation, Information, and Technology, and at the AI for Image-Guided Diagnosis and Therapy chair (Prof. Wiestler), TUM School of Medicine and Health. His current research focuses on vision and multimodal learning for medical image analysis.
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His research background encompasses machine learning for medical image analysis and computer vision for autonomous driving. During his doctoral studies at the Technical University of Munich, he focused on machine learning and image understanding, with a specific emphasis on creating robust algorithms capable of identifying a wide array of unknown anomalies in medical images.
+He earned his B.Sc. and M.Sc. degrees in Computer Science from FAU University in Erlangen, Germany, where he specialized in pattern recognition and medical image analysis.
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diff --git a/author/cosmin-i.-bercea/index.xml b/author/cosmin-i.-bercea/index.xml
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+
+
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+ Cosmin I. Bercea | Computational Imaging and AI in Medicine
+ https://compai-lab.io/author/cosmin-i.-bercea/
+
+ Cosmin I. Bercea
+ Wowchemy (https://wowchemy.com)en-usThu, 25 Jul 2024 00:00:00 +0000
+
+ https://compai-lab.io/author/cosmin-i.-bercea/avatar_huc30509e39f40f5e42a3e593aaa26fe4e_5750588_270x270_fill_q75_lanczos_center.jpg
+ Cosmin I. Bercea
+ https://compai-lab.io/author/cosmin-i.-bercea/
+
+
+
+ AI for Vision-Language Models in Medical Imaging (IN2107)
+ https://compai-lab.io/teaching/vlm_seminar/
+ Thu, 25 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/teaching/vlm_seminar/
+ <p>
+
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+<figure >
+ <div class="d-flex justify-content-center">
+ <div class="w-100" ><img src="https://compai-lab.io/images/vlm_teaser.gif" alt="Teaser" loading="lazy" data-zoomable /></div>
+ </div></figure>
+</p>
+<p><strong>Time</strong>: Wednesday 14-16.</p>
+<p><strong>Location</strong>: - Garching (in-person): FMI, 5610.01.11 <a href="https://nav.tum.de/room/5610.01.011" target="_blank" rel="noopener">https://nav.tum.de/room/5610.01.011</a></p>
+<ul>
+<li>some invited talks on Zoom: <a href="https://tum-conf.zoom-x.de/my/cibercea?pwd=WlMvanU1NUcveUtjVTJrWHAzWFp1dz09" target="_blank" rel="noopener">https://tum-conf.zoom-x.de/my/cibercea?pwd=WlMvanU1NUcveUtjVTJrWHAzWFp1dz09</a></li>
+</ul>
+<p>Vision-language models (VLMs) in medical imaging leverage the integration of visual data and textual information to enhance representation learning. These models can be pre-trained to improve representations, enabling a wide range of downstream applications. This seminar will explore foundational concepts, current methodologies, and recent advancements in applying vision-language models to diverse tasks in medical imaging, such as:</p>
+<ul>
+<li>Synthetic image synthesis</li>
+<li>Anomaly detection</li>
+<li>Clinical report generation</li>
+<li>Visual-question answering</li>
+<li>Classification</li>
+<li>Segmentation</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> or write an e-mail to <a href="mailto:cosmin.bercea@tum.de">cosmin.bercea@tum.de</a></p>
+<p>Check the intro slides here:
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+<figure >
+ <div class="d-flex justify-content-center">
+ <div class="w-100" ><img src="https://compai-lab.io/files/VLM_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div>
+ </div></figure>
+</p>
+<object data="/files/VLM_seminar.pdf" type="application/pdf" width="100%" height="400">
+</object>
+
+
+
+
+ Eleven papers accepted at MICCAI Workshops 2024
+ https://compai-lab.io/post/miccai_workshops_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_workshops_24/
+ <ul>
+<li>
+<p><strong>Selective Test-Time Adaptation using Neural Implicit Representations for Unsupervised Anomaly Detection [Best Paper Award]</strong><br>
+Sameer Ambekar, Julia Schnabel, and Cosmin I. Bercea. <br>
+<a href="https://arxiv.org/abs/2410.03306" target="_blank" rel="noopener">https://arxiv.org/abs/2410.03306</a><br/><br/></p>
+</li>
+<li>
+<p><strong>MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI</strong><br>
+Malek Ben Alaya, Daniel M. Lang, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15270" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15270</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders</strong><br>
+Mehmet Yigit Avci, Emily Chan, Veronika Zimmer, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.03863" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.03863</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models</strong><br>
+Deniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco<br>
+(<a href="https://arxiv.org/abs/2407.16405" target="_blank" rel="noopener">https://arxiv.org/abs/2407.16405</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Graph Neural Networks: A suitable alternative to MLPs in latent 3D medical image classification?</strong><br>
+Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer, Lina Felsner, Jan C. Peeken, Julia A. Schnabel<br>
+(<a href="http://arxiv.org/abs/2407.17219" target="_blank" rel="noopener">http://arxiv.org/abs/2407.17219</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>General Vision Encoder Features as Guidance in Medical Image Registration</strong><br>
+Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa, Ines Machado, Rickmer Braren, Daniel Rückert, Julia A Schnabel, Veronika A Zimmer<br>
+(<a href="https://arxiv.org/abs/2407.13311" target="_blank" rel="noopener">https://arxiv.org/abs/2407.13311</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Language Models Meet Anomaly Detection for Better Interpretability and Generalizability</strong><br>
+Jun Li, Su Hwan Kim, Philip Müller, Lina Felsner, Daniel Rueckert, Benedikt Wiestler, Julia A.Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2404.07622v2" target="_blank" rel="noopener">https://arxiv.org/pdf/2404.07622v2</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer</strong><br>
+Inês P. Machado, Anna Reithmeir, Fryderyk Kogl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James Brenton, Julia Schnabel, Mireia Crispin<br>
+(<a href="https://arxiv.org/abs/2407.17114" target="_blank" rel="noopener">https://arxiv.org/abs/2407.17114</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound</strong><br>
+Hanna Mykula, Lisa Gasser, Silvia Lobmaier, Julia A. Schnabel, Veronika Zimmer, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15119" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15119</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data</strong><br>
+Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2407.12669" target="_blank" rel="noopener">https://arxiv.org/abs/2407.12669</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Complex-valued Federated Learning with Differential Privacy and MRI Applications</strong><br>
+Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia Schnabel, Georgios Kaissis <br>
+([link will be available soon])<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ Seven papers accepted at MICCAI 2024
+ https://compai-lab.io/post/miccai_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_24/
+ <ul>
+<li>
+<p><strong>Diffusion Models with Implicit Guidance for Medical Anomaly Detection</strong><br>
+Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08464" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08464</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI</strong><br>
+Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08298" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08298</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks</strong><br>
+Stefan M. Fischer, Lina Felsner, Daniel M. Lang, Richard Osuala, Johannes Kiechle, Jan C. Peeken, Julia A. Schnabel<br/><br/></p>
+</li>
+<li>
+<p><strong>Interpretable Representation Learning of Cardiac MRI via Attribute Regularization</strong><br>
+Maxime Di Folco, Cosmin I. Bercea, Emily Chan, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2406.08282" target="_blank" rel="noopener">https://arxiv.org/abs/2406.08282</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models</strong><br>
+Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia Schnabel, and Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2403.13890" target="_blank" rel="noopener">https://arxiv.org/abs/2403.13890</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration</strong><br>
+Anna Reithmeir, Lina Felsner, Rickmer Braren, Julia A. Schnabel, Veronika A. Zimmer<br/><br/></p>
+</li>
+<li>
+<p><strong>Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation</strong><br>
+Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rückert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2404.08350" target="_blank" rel="noopener">https://arxiv.org/abs/2404.08350</a>)<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ 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 “Sparse annotation strategies for segmentation of short axis cardiac MRI” (<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>
+
+
+
+ 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:
+
+
+
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+
+
+
+
+
+
+
+<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>
+
+
+
+
+ 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>“<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>“<em>Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening</em>” 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>
+
+
+
+
+
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+ Daniel M. Lang | Computational Imaging and AI in Medicine
+
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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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diff --git a/author/daniel-m.-lang/index.xml b/author/daniel-m.-lang/index.xml
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+
+
+
+ Daniel M. Lang | Computational Imaging and AI in Medicine
+ https://compai-lab.io/author/daniel-m.-lang/
+
+ Daniel M. Lang
+ Wowchemy (https://wowchemy.com)en-usThu, 25 Jul 2024 00:00:00 +0000
+
+ https://compai-lab.io/author/daniel-m.-lang/avatar_hu30628bf96a1bbbf825de70e7e388d37a_614916_270x270_fill_lanczos_center_3.png
+ Daniel M. Lang
+ https://compai-lab.io/author/daniel-m.-lang/
+
+
+
+ Temporal Landmark Tracking on Medical Imaging
+ https://compai-lab.io/old_stuff/teaching/msc_tracking/
+ Thu, 25 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/old_stuff/teaching/msc_tracking/
+ <p>Abstract:</p>
+<p>Even though various learning-based computer vision methods have been developed for pixel tracking, motion estimation in video data depicts a challenging task. Part of the problem arises from the 3D-to-2D projection process that can lead to out-of-plane motion, which impedes long-range pixel trajectory estimation. In the medical domain, video data, i.e. fast magnetic resonance imaging (MRI) sequences, can be used for guidance during treatment. Specifically, in radiation therapy, contouring algorithms are used for tracking of the target volume supposed to receive the main radiation dose during treatment. Delineation can, for example, be performed with a U-Net architecture. However, such an approach only allows for identification of larger structures, while irregular movement can be subtle and localized. Landmark detection models are able to identify such localized regions between different representations of the same object. Furthermore, they are faster than semantic segmentation models, and therefore, allow for computer aided intervention during treatment. In this thesis, different state-of-the-art landmark and pixel tracking algorithms will be tested and further enhanced to identify movement on temporal imaging data of the lungs, i.e. 4D CT. Furthermore, ability of such landmarks to identify movement differing from a normal state, i.e. allowing for identification of anomalies, will be studied.</p>
+
+
+
+
+ Eleven papers accepted at MICCAI Workshops 2024
+ https://compai-lab.io/post/miccai_workshops_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_workshops_24/
+ <ul>
+<li>
+<p><strong>Selective Test-Time Adaptation using Neural Implicit Representations for Unsupervised Anomaly Detection [Best Paper Award]</strong><br>
+Sameer Ambekar, Julia Schnabel, and Cosmin I. Bercea. <br>
+<a href="https://arxiv.org/abs/2410.03306" target="_blank" rel="noopener">https://arxiv.org/abs/2410.03306</a><br/><br/></p>
+</li>
+<li>
+<p><strong>MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI</strong><br>
+Malek Ben Alaya, Daniel M. Lang, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15270" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15270</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders</strong><br>
+Mehmet Yigit Avci, Emily Chan, Veronika Zimmer, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.03863" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.03863</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models</strong><br>
+Deniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco<br>
+(<a href="https://arxiv.org/abs/2407.16405" target="_blank" rel="noopener">https://arxiv.org/abs/2407.16405</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Graph Neural Networks: A suitable alternative to MLPs in latent 3D medical image classification?</strong><br>
+Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer, Lina Felsner, Jan C. Peeken, Julia A. Schnabel<br>
+(<a href="http://arxiv.org/abs/2407.17219" target="_blank" rel="noopener">http://arxiv.org/abs/2407.17219</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>General Vision Encoder Features as Guidance in Medical Image Registration</strong><br>
+Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa, Ines Machado, Rickmer Braren, Daniel Rückert, Julia A Schnabel, Veronika A Zimmer<br>
+(<a href="https://arxiv.org/abs/2407.13311" target="_blank" rel="noopener">https://arxiv.org/abs/2407.13311</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Language Models Meet Anomaly Detection for Better Interpretability and Generalizability</strong><br>
+Jun Li, Su Hwan Kim, Philip Müller, Lina Felsner, Daniel Rueckert, Benedikt Wiestler, Julia A.Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2404.07622v2" target="_blank" rel="noopener">https://arxiv.org/pdf/2404.07622v2</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer</strong><br>
+Inês P. Machado, Anna Reithmeir, Fryderyk Kogl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James Brenton, Julia Schnabel, Mireia Crispin<br>
+(<a href="https://arxiv.org/abs/2407.17114" target="_blank" rel="noopener">https://arxiv.org/abs/2407.17114</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound</strong><br>
+Hanna Mykula, Lisa Gasser, Silvia Lobmaier, Julia A. Schnabel, Veronika Zimmer, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15119" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15119</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data</strong><br>
+Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2407.12669" target="_blank" rel="noopener">https://arxiv.org/abs/2407.12669</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Complex-valued Federated Learning with Differential Privacy and MRI Applications</strong><br>
+Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia Schnabel, Georgios Kaissis <br>
+([link will be available soon])<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ Paper Accepted at MELBA Journal
+ https://compai-lab.io/post/fischer_melba_24/
+ Fri, 14 Jun 2024 00:00:00 +0000
+ https://compai-lab.io/post/fischer_melba_24/
+ <p>Stefan M. Fischer’s submission to the MICCAI2023 Lymph Node Quantification Challenge won the 3rd price.<br>
+Therefore, the challenge team was invited for a presentation at MICCAI 2023 and to a Special Issue Submission at the MELBA Journal.
+The journal submission “<em>Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge</em>” is now available at MELBA.<br>
+The paper is available <a href="https://www.melba-journal.org/papers/2024:008.html" target="_blank" rel="noopener">here</a>.</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 “Sparse annotation strategies for segmentation of short axis cardiac MRI” (<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>
+
+
+
+
diff --git a/author/daniel-rueckert/index.html b/author/daniel-rueckert/index.html
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+ Daniel Rueckert | Computational Imaging and AI in Medicine
+
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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.
Fryderyk Kögl is a PhD student at the Chair of Computational Imaging and AI in Medicine at the Technical University Munich (TUM). He received his B.Sc. in Engineering Science and M.Sc. in Biomedical Computing from TUM. In his Master’s thesis at the Harvard Medical School he curated the largest public dataset for pre- to post-MR/iMR/US registration, developed a 3D Slicer extension for data curation, developed a low-cost and tool-free neuronavigation method and worked on deep learning patch-based registration. His research interests lie in deep Learning-based image registration, data curation & visualisation and neuronavigation.
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
+
+
+
Reliable artificial intelligence
+
+
Medical image computing
+
+
Probabilistic methods
+
+
+
+
+
+
+
+
Education
+
+
+
+
+
+
PostDoc in AI for medical imaging
+
Imperial College London, UK
+
+
+
+
+
+
+
Specialist Radiologist
+
Technical University of Munich, Germany
+
+
+
+
+
+
+
Doctorate in molecular medicine and systems biology (Dr. med.)
Ha Young Kim is a PhD student at the Chair of Computational Imaging and AI in Medicine at TU Munich.
+She received her M.Sc. in Biomedical Computing from TU Munich with a focus on magnetic resonance image reconstruction and postprocessing.
+In her Master’s thesis at GE HealthCare, she demonstrated the feasibility of using deep learning reconstruction for quantitative transient-state imaging on prostate imaging.
+Her research interests lie in the analysis and development magnetic resonance imaging in combination with machine learning algorithms.
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).
+
+
+
+
+
+
+
+
Interests
+
+
+
Brain Magnetic Resonance Imaging
+
+
Image reconstruction & Motion Correction
+
+
Deep learning
+
+
+
+
+
+
+
+
Education
+
+
+
+
+
+
MSc in Bio- and Medical Physics, 2021
+
Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
+
+
+
+
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diff --git a/author/hannah-eichhorn/index.xml b/author/hannah-eichhorn/index.xml
new file mode 100644
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+++ b/author/hannah-eichhorn/index.xml
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+
+
+
+ Hannah Eichhorn | Computational Imaging and AI in Medicine
+ https://compai-lab.io/author/hannah-eichhorn/
+
+ Hannah Eichhorn
+ Wowchemy (https://wowchemy.com)en-usFri, 05 Jul 2024 00:00:00 +0000
+
+ https://compai-lab.io/author/hannah-eichhorn/avatar_hu9531ded0c7e72b6a4aaaeb59428f1070_698566_270x270_fill_q75_lanczos_center.jpg
+ Hannah Eichhorn
+ https://compai-lab.io/author/hannah-eichhorn/
+
+
+
+ Seven papers accepted at MICCAI 2024
+ https://compai-lab.io/post/miccai_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_24/
+ <ul>
+<li>
+<p><strong>Diffusion Models with Implicit Guidance for Medical Anomaly Detection</strong><br>
+Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08464" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08464</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI</strong><br>
+Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08298" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08298</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks</strong><br>
+Stefan M. Fischer, Lina Felsner, Daniel M. Lang, Richard Osuala, Johannes Kiechle, Jan C. Peeken, Julia A. Schnabel<br/><br/></p>
+</li>
+<li>
+<p><strong>Interpretable Representation Learning of Cardiac MRI via Attribute Regularization</strong><br>
+Maxime Di Folco, Cosmin I. Bercea, Emily Chan, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2406.08282" target="_blank" rel="noopener">https://arxiv.org/abs/2406.08282</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models</strong><br>
+Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia Schnabel, and Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2403.13890" target="_blank" rel="noopener">https://arxiv.org/abs/2403.13890</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration</strong><br>
+Anna Reithmeir, Lina Felsner, Rickmer Braren, Julia A. Schnabel, Veronika A. Zimmer<br/><br/></p>
+</li>
+<li>
+<p><strong>Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation</strong><br>
+Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rückert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2404.08350" target="_blank" rel="noopener">https://arxiv.org/abs/2404.08350</a>)<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ Hannah Eichhorn elected as ISMRM Study Group Trainee Representative
+ https://compai-lab.io/post/eichhorn_study_group_5_24/
+ Thu, 23 May 2024 00:00:00 +0000
+ https://compai-lab.io/post/eichhorn_study_group_5_24/
+ <p>Hannah Eichhorn has been elected as Trainee Representative of the ISMRM Motion Detection & Correction Study Group. She started her term at the ISMRM Annual Meeting in Singapore in the beginning of May.</p>
+<p>The Study Group’s mission is to investigate how various forms of motion can affect MR data, how motion can be detected, how to deal best with motion-corrupted data, and what can be done to prevent MR data from getting corrupted by motion.</p>
+
+
+
+
+ Two abstracts accepted at 2024 ISMRM & ISMRT Annual Meeting (oral talks)
+ https://compai-lab.io/post/spieker_eichhorn_ismrm24/
+ Thu, 01 Feb 2024 00:00:00 +0000
+ https://compai-lab.io/post/spieker_eichhorn_ismrm24/
+ <p>Veronika Spieker’s and Hannah Eichhorn’s abstracts have been accepted to be presented as orals at the 2024 ISMRM & ISMRT Annual Meeting.</p>
+<p>Hannah Eichhorn will present her work “<em>PHIMO: Physics-Informed Motion Correction of GRE MRI for T2</em> Quantification*” on Tuesday, 07 May 2024 at 8:15 am SGT. Check <a href="https://github.com/HannahEichhorn/PHIMO" target="_blank" rel="noopener">this GitHub repository</a> for more information.</p>
+<p>Veronika Spieker will present her work “<em>DE-NIK: Leveraging Dual-Echo Data for Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit k-Space Representations</em>” on Monday, 06 May 2024 at 8:15 am SGT. Check <a href="https://github.com/vjspi/DE-NIK" target="_blank" rel="noopener">this GitHub repository</a> for more information.</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>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+<figure >
+ <div class="d-flex justify-content-center">
+ <div class="w-100" ><img alt="img" srcset="
+ /post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_e1ff7f723fc5ed308be173642a5f92f5.webp 400w,
+ /post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_59a22aa363f30bc9c49ab63c04f6c200.webp 760w,
+ /post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_1200x1200_fit_q75_h2_lanczos_3.webp 1200w"
+ src="https://compai-lab.io/post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_e1ff7f723fc5ed308be173642a5f92f5.webp"
+ width="760"
+ height="713"
+ loading="lazy" data-zoomable /></div>
+ </div></figure>
+</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 “Sparse annotation strategies for segmentation of short axis cardiac MRI” (<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’s and Hannah Eichhorn’s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM & ISMRT Annual Meeting.</p>
+<p>Veronika Spieker will present her work on “<em>Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction</em>” on Monday, 05 June 2023 at 1:45 pm EDT.</p>
+<p>Hannah Eichhorn will present her work on “<em>Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations</em>” 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>
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+ Ihsane Olakorede | Computational Imaging and AI in Medicine
+
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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
+
+
+
Shape Analysis
+
+
Representation Learning
+
+
Magnetic Resonance Imaging
+
+
+
+
+
+
+
+
Education
+
+
+
+
+
+
M.Sc. in Electrical and Computer Engineering, 2023
+
Technical University of Munich
+
+
+
+
+
+
+
B.Eng. in Electrical Engineering and Information Technology, 2020
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.
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
+
University of Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology
Laura Daza is a postdoctoral researcher at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich. She received her Ph.D. at the Research and Formation in Artificial Intelligence (CINFONIA) at Universidad de los Andes advised by Pablo Arbeláez and did an internship with Professor René Vidal at Johns Hopkins University. Her areas of interest are Computer Vision, Machine Learning and Deep Learning, as well as their application to biomedical problems. During her Ph.D., her research was focused on the analysis of adversarial robustness of medical image analysis methods and natural image and video classification methods. She also worked on early lung cancer diagnosis leveraging multimodal data, pharmaceutical discovery, and automatic bone age assesment in children.
Lina Felsner is a postdoctoral researcher at the Chair of Computational Imaging and AI in Medicine at TU Munich.
+She received her B.Sc. and M.Sc. in Medical Imaging from FAU Erlangen-Nürnberg with a specialization in Mediacl Image and Data Processing.
+During her Ph.D at the Pattern Recognition Lab at FAU Lina worked on Advanced 3-D Reconstruction of Talbot Lau Data.
+From 2022 to 2023 Lina was a postdoctoral Research Assistant at the King’s College London working on the motion corrected reconstruction of cardiovascular MR data.
+Her research interests lie at the intersection of Medical Image Computing, Inverse Problems, and Machine Learning, where she explores novel algorithms and methodologies to enhance medical imaging techniques and diagnostic accuracy.
Marta Hasny is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and the Technical University of Munich (TUM). She received her B.Sc. in Computer Science from Pace University and completed her M.Sc. in Biomedical Computing at TUM. For her master’s thesis at Harvard Medical School, she worked on improving the visualization of myocardial scar in late gadolinium enhancement cardiac MR using diffusion models. Her research interests include generative AI, foundation models, and their applications in cardiology.
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
+
+
Cardiac imaging
+
+
+
+
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+
+
Education
+
+
+
+
+
+
PhD in Artificial Intelligence, 2021
+
Université de Lyon, CREATIS Laboratory
+
+
+
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+
+
MEng in Image Processing, 2018
+
CPE Lyon
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+
MSc in Image development and 3D technologies, 2018
+
Université Lyon 1
+
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+
Teaching
+
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+
From Self-supervised Learning to Foundation Models in Medical Imaging
+
Master seminar | WS24/25 |
+
+
+
+
+
+
Learning of and on Manifolds in Medical Imaging
+
Master seminar | WS23/24 |
+
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+
Interpretable AI in Medical Imaging
+
Master seminar | SS22/23 |
+
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+
Student Projects & Theses
+
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+
Exploring SPD Feature Descriptors for Medical Image Classification
+
Master's Thesis | 2024 | Josef Mayr | ongoing |
+
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+
Conditional 3D Cardiac MRI Synthesis Using Differentially Private Latent Diffusion Models
+
Master's Thesis | 2024 | Deniz Daum | |
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+
Segmentation of sparse annotated data - application to cardiac imaging
+
Master's Thesis | 2023 | Joshua Stein | |
+
+
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+
Segmentation and morphological comparison of distal radial articular surfaces
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diff --git a/author/maxime-di-folco/index.xml b/author/maxime-di-folco/index.xml
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+
+
+
+ Maxime Di Folco | Computational Imaging and AI in Medicine
+ https://compai-lab.io/author/maxime-di-folco/
+
+ Maxime Di Folco
+ Wowchemy (https://wowchemy.com)en-usTue, 13 Aug 2024 00:00:00 +0000
+
+ https://compai-lab.io/author/maxime-di-folco/avatar_hub43e7a5903b40f0a40369ad9f2cf0390_106345_270x270_fill_q75_lanczos_center.jpg
+ Maxime Di Folco
+ https://compai-lab.io/author/maxime-di-folco/
+
+
+
+ Latent Functional Maps for Medical Imaging
+ https://compai-lab.io/vacancies/msc_functionalmaps/
+ Tue, 13 Aug 2024 00:00:00 +0000
+ https://compai-lab.io/vacancies/msc_functionalmaps/
+ <p>Abstract:</p>
+<p>Neural Networks (NNs) learn to represent high-dimensional data as elements of lower-dimensional latent spaces. Modeling the relationships between these representational spaces is an ongoing challenge. Successfully addressing this challenge could enable the reuse of representations in downstream tasks, reducing the need to retrain similar models multiple times. Recently, Fumero et al. leveraged the internal geometry of representations and proposed applying latent functional maps to align representations across distinct models, demonstrating its relevance for comparing representations. However, these kinds of approaches have not yet been explored in the context of medical imaging datasets, where aligning multimodal representa-
+tions could significantly enhance the effectiveness of models in medical applications. This project aims to use latent functional maps to align multimodal medical representations (e.g., text and vision). The first part of the thesis will involve a literature review on representation similarity. This will be followed by experimenting with the latent functional maps approach on a toy dataset of medical images and later applying it to real medical imaging tasks.</p>
+
+
+
+
+ Eleven papers accepted at MICCAI Workshops 2024
+ https://compai-lab.io/post/miccai_workshops_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_workshops_24/
+ <ul>
+<li>
+<p><strong>Selective Test-Time Adaptation using Neural Implicit Representations for Unsupervised Anomaly Detection [Best Paper Award]</strong><br>
+Sameer Ambekar, Julia Schnabel, and Cosmin I. Bercea. <br>
+<a href="https://arxiv.org/abs/2410.03306" target="_blank" rel="noopener">https://arxiv.org/abs/2410.03306</a><br/><br/></p>
+</li>
+<li>
+<p><strong>MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI</strong><br>
+Malek Ben Alaya, Daniel M. Lang, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15270" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15270</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders</strong><br>
+Mehmet Yigit Avci, Emily Chan, Veronika Zimmer, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.03863" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.03863</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models</strong><br>
+Deniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco<br>
+(<a href="https://arxiv.org/abs/2407.16405" target="_blank" rel="noopener">https://arxiv.org/abs/2407.16405</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Graph Neural Networks: A suitable alternative to MLPs in latent 3D medical image classification?</strong><br>
+Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer, Lina Felsner, Jan C. Peeken, Julia A. Schnabel<br>
+(<a href="http://arxiv.org/abs/2407.17219" target="_blank" rel="noopener">http://arxiv.org/abs/2407.17219</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>General Vision Encoder Features as Guidance in Medical Image Registration</strong><br>
+Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa, Ines Machado, Rickmer Braren, Daniel Rückert, Julia A Schnabel, Veronika A Zimmer<br>
+(<a href="https://arxiv.org/abs/2407.13311" target="_blank" rel="noopener">https://arxiv.org/abs/2407.13311</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Language Models Meet Anomaly Detection for Better Interpretability and Generalizability</strong><br>
+Jun Li, Su Hwan Kim, Philip Müller, Lina Felsner, Daniel Rueckert, Benedikt Wiestler, Julia A.Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2404.07622v2" target="_blank" rel="noopener">https://arxiv.org/pdf/2404.07622v2</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer</strong><br>
+Inês P. Machado, Anna Reithmeir, Fryderyk Kogl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James Brenton, Julia Schnabel, Mireia Crispin<br>
+(<a href="https://arxiv.org/abs/2407.17114" target="_blank" rel="noopener">https://arxiv.org/abs/2407.17114</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound</strong><br>
+Hanna Mykula, Lisa Gasser, Silvia Lobmaier, Julia A. Schnabel, Veronika Zimmer, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15119" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15119</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data</strong><br>
+Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2407.12669" target="_blank" rel="noopener">https://arxiv.org/abs/2407.12669</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Complex-valued Federated Learning with Differential Privacy and MRI Applications</strong><br>
+Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia Schnabel, Georgios Kaissis <br>
+([link will be available soon])<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ Seven papers accepted at MICCAI 2024
+ https://compai-lab.io/post/miccai_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_24/
+ <ul>
+<li>
+<p><strong>Diffusion Models with Implicit Guidance for Medical Anomaly Detection</strong><br>
+Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08464" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08464</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI</strong><br>
+Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08298" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08298</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks</strong><br>
+Stefan M. Fischer, Lina Felsner, Daniel M. Lang, Richard Osuala, Johannes Kiechle, Jan C. Peeken, Julia A. Schnabel<br/><br/></p>
+</li>
+<li>
+<p><strong>Interpretable Representation Learning of Cardiac MRI via Attribute Regularization</strong><br>
+Maxime Di Folco, Cosmin I. Bercea, Emily Chan, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2406.08282" target="_blank" rel="noopener">https://arxiv.org/abs/2406.08282</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models</strong><br>
+Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia Schnabel, and Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2403.13890" target="_blank" rel="noopener">https://arxiv.org/abs/2403.13890</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration</strong><br>
+Anna Reithmeir, Lina Felsner, Rickmer Braren, Julia A. Schnabel, Veronika A. Zimmer<br/><br/></p>
+</li>
+<li>
+<p><strong>Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation</strong><br>
+Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rückert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2404.08350" target="_blank" rel="noopener">https://arxiv.org/abs/2404.08350</a>)<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ 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 “Sparse annotation strategies for segmentation of short axis cardiac MRI” (<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>
+
+
+
+
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+ Michael Brady | Computational Imaging and AI in Medicine
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Natascha Niessen pursues her PhD project in a joint collaboration between GE Healthcare and the Chair of Computational Imaging and AI in Medicine at TU Munich, as well as the department of psychiatry at LMU. As part of her French-German double-degree she received her Engineering Diploma (M.Sc.) from CentraleSupélec and her M.Sc. in Electrical Engineering from TU Munich with a focus on medical imaging and machine learning. In her Master‘s thesis at Stanford University, she developed a novel approach for validating multi- compartment fitting algorithms for brain magnetic resonance imaging (MRI). Her research interests lie in the development of Deep Learning-enabled MRI reconstruction and early Alzheimer’s Disease prediction as part of the european PREDICTOM project.
Richard Osuala is a Ph.D. student at the University of Barcelona (BCN-AIM Lab) and visiting researcher at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University of Munich (TUM). After working for 4 years as data scientist and AI solution architect in industry, his research focuses on generative AI to solve medical imaging problems. His work is part of the European projects EuCanImage (H2020) and RadioVal (Horizon Europe) investigating and creating machine learning solutions for medical image analysis with the goal of enhancing cancer diagnosis and treatment.
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.
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.
+
+
+
Open Theses and Projects:
+Looking for Bachelor / Master Thesis for Summer semester 2025. Please reach out via email or website - https://ambekarsameer.com if you are interested in working on Test-time adaptation, Domain Generalization, Meta Learning or related topics.
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.
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.
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.
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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MRI Reconstruction
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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
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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diff --git a/author/veronika-zimmer/index.xml b/author/veronika-zimmer/index.xml
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+ Veronika Zimmer | Computational Imaging and AI in Medicine
+ https://compai-lab.io/author/veronika-zimmer/
+
+ Veronika Zimmer
+ Wowchemy (https://wowchemy.com)en-usMon, 21 Nov 2022 00:00:00 +0000
+
+ https://compai-lab.io/author/veronika-zimmer/avatar_hu7d3339efd454f84887db003b20ae29f7_22328_270x270_fill_q75_lanczos_center.jpg
+ Veronika Zimmer
+ https://compai-lab.io/author/veronika-zimmer/
+
+
+
+ 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>
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+ 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/
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+ 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/
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+ Vicky Goh | Computational Imaging and AI in Medicine
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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+ [{"authors":null,"categories":null,"content":"Cosmin Bercea is a postdoctoral researcher at the Computational Imaging and AI in Medicine chair (Prof. Schnabel), TUM School of Computation, Information, and Technology, and at the AI for Image-Guided Diagnosis and Therapy chair (Prof. Wiestler), TUM School of Medicine and Health. His current research focuses on vision and multimodal learning for medical image analysis.\nHis research background encompasses machine learning for medical image analysis and computer vision for autonomous driving. During his doctoral studies at the Technical University of Munich, he focused on machine learning and image understanding, with a specific emphasis on creating robust algorithms capable of identifying a wide array of unknown anomalies in medical images. He earned his B.Sc. and M.Sc. degrees in Computer Science from FAU University in Erlangen, Germany, where he specialized in pattern recognition and medical image analysis.\n","date":1721865600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1721865600,"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 postdoctoral researcher at the Computational Imaging and AI in Medicine chair (Prof. Schnabel), TUM School of Computation, Information, and Technology, and at the AI for Image-Guided Diagnosis and Therapy chair (Prof.","tags":null,"title":"Cosmin I. Bercea","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 Open Theses and Projects: Looking for Bachelor / Master Thesis for Summer semester 2025. Please reach out via email or website - https://ambekarsameer.com if you are interested in working on Test-time adaptation, Domain Generalization, Meta Learning or related topics. ","date":1720137600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1720137600,"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":"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":"Laura Daza is a postdoctoral researcher at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich. She received her Ph.D. at the Research and Formation in Artificial Intelligence (CINFONIA) at Universidad de los Andes advised by Pablo Arbeláez and did an internship with Professor René Vidal at Johns Hopkins University. Her areas of interest are Computer Vision, Machine Learning and Deep Learning, as well as their application to biomedical problems. During her Ph.D., her research was focused on the analysis of adversarial robustness of medical image analysis methods and natural image and video classification methods. She also worked on early lung cancer diagnosis leveraging multimodal data, pharmaceutical discovery, and automatic bone age assesment in children.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"d0f4c18adf64929bd668ba8bd9e21346","permalink":"https://compai-lab.io/author/laura-daza/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/laura-daza/","section":"authors","summary":"Laura Daza is a postdoctoral researcher at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich. She received her Ph.D. at the Research and Formation in Artificial Intelligence (CINFONIA) at Universidad de los Andes advised by Pablo Arbeláez and did an internship with Professor René Vidal at Johns Hopkins University.","tags":null,"title":"Laura Daza","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":"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":1723507200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1723507200,"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":"Lina Felsner is a postdoctoral researcher at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her B.Sc. and M.Sc. in Medical Imaging from FAU Erlangen-Nürnberg with a specialization in Mediacl Image and Data Processing. During her Ph.D at the Pattern Recognition Lab at FAU Lina worked on Advanced 3-D Reconstruction of Talbot Lau Data. From 2022 to 2023 Lina was a postdoctoral Research Assistant at the King’s College London working on the motion corrected reconstruction of cardiovascular MR data. Her research interests lie at the intersection of Medical Image Computing, Inverse Problems, and Machine Learning, where she explores novel algorithms and methodologies to enhance medical imaging techniques and diagnostic accuracy.\n","date":1720137600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1720137600,"objectID":"e729628c82b6441ab4c1eefdd23fb1c7","permalink":"https://compai-lab.io/author/lina-felsner/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/lina-felsner/","section":"authors","summary":"Lina Felsner is a postdoctoral researcher at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her B.Sc. and M.Sc. in Medical Imaging from FAU Erlangen-Nürnberg with a specialization in Mediacl Image and Data Processing.","tags":null,"title":"Lina Felsner","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":"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":"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":1721865600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1721865600,"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":"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":1720137600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1720137600,"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":"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":1720137600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1720137600,"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":"Marta Hasny is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and the Technical University of Munich (TUM). She received her B.Sc. in Computer Science from Pace University and completed her M.Sc. in Biomedical Computing at TUM. For her master’s thesis at Harvard Medical School, she worked on improving the visualization of myocardial scar in late gadolinium enhancement cardiac MR using diffusion models. Her research interests include generative AI, foundation models, and their applications in cardiology.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"01505f705e18f22d55e6ae9659ce16b1","permalink":"https://compai-lab.io/author/marta-hasny/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/marta-hasny/","section":"authors","summary":"Marta Hasny is a PhD student at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and the Technical University of Munich (TUM). She received her B.","tags":null,"title":"Marta Hasny","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":1718323200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1718323200,"objectID":"0a33e07e827b03a8f60c960b3beac217","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":"Ha Young Kim is a PhD student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her M.Sc. in Biomedical Computing from TU Munich with a focus on magnetic resonance image reconstruction and postprocessing. In her Master’s thesis at GE HealthCare, she demonstrated the feasibility of using deep learning reconstruction for quantitative transient-state imaging on prostate imaging. Her research interests lie in the analysis and development magnetic resonance imaging in combination with machine learning algorithms.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"9ce11e2951f5d7c4d0b618ee41a16f79","permalink":"https://compai-lab.io/author/ha-young-kim/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/ha-young-kim/","section":"authors","summary":"Ha Young Kim is a PhD student at the Chair of Computational Imaging and AI in Medicine at TU Munich. She received her M.Sc. in Biomedical Computing from TU Munich with a focus on magnetic resonance image reconstruction and postprocessing.","tags":null,"title":"Ha Young Kim","type":"authors"},{"authors":null,"categories":null,"content":"Fryderyk Kögl is a PhD student at the Chair of Computational Imaging and AI in Medicine at the Technical University Munich (TUM). He received his B.Sc. in Engineering Science and M.Sc. in Biomedical Computing from TUM. In his Master’s thesis at the Harvard Medical School he curated the largest public dataset for pre- to post-MR/iMR/US registration, developed a 3D Slicer extension for data curation, developed a low-cost and tool-free neuronavigation method and worked on deep learning patch-based registration. His research interests lie in deep Learning-based image registration, data curation \u0026amp; visualisation and neuronavigation.\n","date":1721865600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1721865600,"objectID":"9a61fc7961f0d1c4b9fe3d35d103cbbf","permalink":"https://compai-lab.io/author/fryderyk-kogl/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/fryderyk-kogl/","section":"authors","summary":"Fryderyk Kögl is a PhD student at the Chair of Computational Imaging and AI in Medicine at the Technical University Munich (TUM). He received his B.Sc. in Engineering Science and M.","tags":null,"title":"Fryderyk Kögl","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":1721865600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1721865600,"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":"Richard Osuala is a Ph.D. student at the University of Barcelona (BCN-AIM Lab) and visiting researcher at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University of Munich (TUM). After working for 4 years as data scientist and AI solution architect in industry, his research focuses on generative AI to solve medical imaging problems. His work is part of the European projects EuCanImage (H2020) and RadioVal (Horizon Europe) investigating and creating machine learning solutions for medical image analysis with the goal of enhancing cancer diagnosis and treatment.\n","date":1720137600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1720137600,"objectID":"cd733108e38d362e8ad7fa04d9f11bb8","permalink":"https://compai-lab.io/author/richard-osuala/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/richard-osuala/","section":"authors","summary":"Richard Osuala is a Ph.D. student at the University of Barcelona (BCN-AIM Lab) and visiting researcher at the Institute of Machine Learning for Biomedical Imaging (IML) at Helmholtz Center Munich and Technical University of Munich (TUM).","tags":null,"title":"Richard Osuala","type":"authors"},{"authors":null,"categories":null,"content":"Natascha Niessen pursues her PhD project in a joint collaboration between GE Healthcare and the Chair of Computational Imaging and AI in Medicine at TU Munich, as well as the department of psychiatry at LMU. As part of her French-German double-degree she received her Engineering Diploma (M.Sc.) from CentraleSupélec and her M.Sc. in Electrical Engineering from TU Munich with a focus on medical imaging and machine learning. In her Master‘s thesis at Stanford University, she developed a novel approach for validating multi- compartment fitting algorithms for brain magnetic resonance imaging (MRI). Her research interests lie in the development of Deep Learning-enabled MRI reconstruction and early Alzheimer’s Disease prediction as part of the european PREDICTOM project.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"78853944e36213de090ec5edb433214d","permalink":"https://compai-lab.io/author/natascha-niessen/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/natascha-niessen/","section":"authors","summary":"Natascha Niessen pursues her PhD project in a joint collaboration between GE Healthcare and the Chair of Computational Imaging and AI in Medicine at TU Munich, as well as the department of psychiatry at LMU.","tags":null,"title":"Natascha Niessen","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 current research interests lie in data-driven models for image registration, physics-inspired regularization, and Riemannian manifolds.\n","date":1721865600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1721865600,"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":"Chun Kit Wong is a Ph.D. student at the Technical University of Denmark (SONAI project group), working on translating AI to fetal ultrasound clinic. Prior to this he studied liver diseases with histology images in the industry. Even earlier than that he was with an academic lab in Singapore, where he worked on MRI image analysis and sequence programming, in addition to providing research computing support to the lab.\n","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"13b5ff03f3b94abf1fc4aa913db1948c","permalink":"https://compai-lab.io/author/chun-kit-wong/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/chun-kit-wong/","section":"authors","summary":"Chun Kit Wong is a Ph.D. student at the Technical University of Denmark (SONAI project group), working on translating AI to fetal ultrasound clinic. Prior to this he studied liver diseases with histology images in the industry.","tags":null,"title":"Chun Kit Wong","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":1720137600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1720137600,"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":"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":1720137600,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1720137600,"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":1715299200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1715299200,"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":["Maxime Di Folco"],"categories":null,"content":"Abstract:\nNeural Networks (NNs) learn to represent high-dimensional data as elements of lower-dimensional latent spaces. Modeling the relationships between these representational spaces is an ongoing challenge. Successfully addressing this challenge could enable the reuse of representations in downstream tasks, reducing the need to retrain similar models multiple times. Recently, Fumero et al. leveraged the internal geometry of representations and proposed applying latent functional maps to align representations across distinct models, demonstrating its relevance for comparing representations. However, these kinds of approaches have not yet been explored in the context of medical imaging datasets, where aligning multimodal representa- tions could significantly enhance the effectiveness of models in medical applications. This project aims to use latent functional maps to align multimodal medical representations (e.g., text and vision). The first part of the thesis will involve a literature review on representation similarity. This will be followed by experimenting with the latent functional maps approach on a toy dataset of medical images and later applying it to real medical imaging tasks.\n","date":1723507200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1723507200,"objectID":"90b4bdc17d0b871c84962df5694779e8","permalink":"https://compai-lab.io/vacancies/msc_functionalmaps/","publishdate":"2024-08-13T00:00:00Z","relpermalink":"/vacancies/msc_functionalmaps/","section":"vacancies","summary":"Master Thesis.","tags":["master"],"title":"Latent Functional Maps for Medical Imaging","type":"vacancies"},{"authors":["Cosmin I. Bercea","Jun Li"],"categories":null,"content":" Time: Wednesday 14-16.\nLocation: - Garching (in-person): FMI, 5610.01.11 https://nav.tum.de/room/5610.01.011\n some invited talks on Zoom: https://tum-conf.zoom-x.de/my/cibercea?pwd=WlMvanU1NUcveUtjVTJrWHAzWFp1dz09 Vision-language models (VLMs) in medical imaging leverage the integration of visual data and textual information to enhance representation learning. These models can be pre-trained to improve representations, enabling a wide range of downstream applications. This seminar will explore foundational concepts, current methodologies, and recent advancements in applying vision-language models to diverse tasks in medical imaging, such as:\n Synthetic image synthesis Anomaly detection Clinical report generation Visual-question answering Classification Segmentation Please register via the TUM matching system: https://matching.in.tum.de or write an e-mail to cosmin.bercea@tum.de\nCheck the intro slides here: ","date":1721865600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1721865600,"objectID":"6757e50e072af635a687b017b87aae27","permalink":"https://compai-lab.io/teaching/vlm_seminar/","publishdate":"2024-07-25T00:00:00Z","relpermalink":"/teaching/vlm_seminar/","section":"teaching","summary":"Winter semester 2024. TUM Informatics. Master Seminar.","tags":["ws24"],"title":"AI for Vision-Language Models in Medical Imaging (IN2107)","type":"teaching"},{"authors":["Anna Reithmeir","Fryderyk Kögl"],"categories":null,"content":"Time: Wednesday 10-12 a.m.\nLocation: Garching (in-person)\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 Requirements:\n Background in image processing and machine learning Interest in medical image analysis Goal and organization:\nThe participating students will learn the fundamental concepts of image registration. They will acquire the skills to analyze critically state-of-the-art research work and to define own research questions. Basic concepts will be introduced with an overview of different research topics. The participants will select a research paper (suggestions given by the lecturers) and independently work on it with a final oral presentation and a written report. Presentations of members of international research groups will provide the students with insights into state-of-the-art research in the field.\nPlease register via the TUM matching system: https://matching.in.tum.de or write an email to anna.reithmeir@tum.de.\nThe seminar will take place Wednesdays from 10 a.m. to 12.a.m. in Garching.\n","date":1721865600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1721865600,"objectID":"a783b7a59ed512774297091f3c94af00","permalink":"https://compai-lab.io/old_stuff/teaching/registration_seminar_ws24/","publishdate":"2024-07-25T00:00:00Z","relpermalink":"/old_stuff/teaching/registration_seminar_ws24/","section":"old_stuff","summary":"Winter semester 2024. TUM Informatics. Master Seminar.","tags":["ws24"],"title":"Master Seminar - Medical Image Registration (IN2107, IN4462)","type":"old_stuff"},{"authors":["Daniel M. Lang"],"categories":null,"content":"Abstract:\nEven though various learning-based computer vision methods have been developed for pixel tracking, motion estimation in video data depicts a challenging task. Part of the problem arises from the 3D-to-2D projection process that can lead to out-of-plane motion, which impedes long-range pixel trajectory estimation. In the medical domain, video data, i.e. fast magnetic resonance imaging (MRI) sequences, can be used for guidance during treatment. Specifically, in radiation therapy, contouring algorithms are used for tracking of the target volume supposed to receive the main radiation dose during treatment. Delineation can, for example, be performed with a U-Net architecture. However, such an approach only allows for identification of larger structures, while irregular movement can be subtle and localized. Landmark detection models are able to identify such localized regions between different representations of the same object. Furthermore, they are faster than semantic segmentation models, and therefore, allow for computer aided intervention during treatment. In this thesis, different state-of-the-art landmark and pixel tracking algorithms will be tested and further enhanced to identify movement on temporal imaging data of the lungs, i.e. 4D CT. Furthermore, ability of such landmarks to identify movement differing from a normal state, i.e. allowing for identification of anomalies, will be studied.\n","date":1721865600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1721865600,"objectID":"4df7786cf33b927f02d2e0c11da54bec","permalink":"https://compai-lab.io/old_stuff/teaching/msc_tracking/","publishdate":"2024-07-25T00:00:00Z","relpermalink":"/old_stuff/teaching/msc_tracking/","section":"old_stuff","summary":"Master Thesis.","tags":["master"],"title":"Temporal Landmark Tracking on Medical Imaging","type":"old_stuff"},{"authors":["Sameer Ambekar","Cosmin I. Bercea","Maxime Di Folco","Lina Felsner","Fryderyk Kögl","Daniel M. Lang","Jun Li","Richard Osuala","Anna Reithmeir","Anneliese Riess"],"categories":null,"content":" Selective Test-Time Adaptation using Neural Implicit Representations for Unsupervised Anomaly Detection [Best Paper Award]\nSameer Ambekar, Julia Schnabel, and Cosmin I. Bercea. https://arxiv.org/abs/2410.03306\n MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI\nMalek Ben Alaya, Daniel M. Lang, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea\n(https://arxiv.org/pdf/2407.15270)\n Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders\nMehmet Yigit Avci, Emily Chan, Veronika Zimmer, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea\n(https://arxiv.org/pdf/2407.03863)\n On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models\nDeniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco\n(https://arxiv.org/abs/2407.16405)\n Graph Neural Networks: A suitable alternative to MLPs in latent 3D medical image classification?\nJohannes Kiechle, Daniel M. Lang, Stefan M. Fischer, Lina Felsner, Jan C. Peeken, Julia A. Schnabel\n(http://arxiv.org/abs/2407.17219)\n General Vision Encoder Features as Guidance in Medical Image Registration\nFryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa, Ines Machado, Rickmer Braren, Daniel Rückert, Julia A Schnabel, Veronika A Zimmer\n(https://arxiv.org/abs/2407.13311)\n Language Models Meet Anomaly Detection for Better Interpretability and Generalizability\nJun Li, Su Hwan Kim, Philip Müller, Lina Felsner, Daniel Rueckert, Benedikt Wiestler, Julia A.Schnabel, and Cosmin I. Bercea\n(https://arxiv.org/pdf/2404.07622v2)\n A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer\nInês P. Machado, Anna Reithmeir, Fryderyk Kogl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James Brenton, Julia Schnabel, Mireia Crispin\n(https://arxiv.org/abs/2407.17114)\n Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound\nHanna Mykula, Lisa Gasser, Silvia Lobmaier, Julia A. Schnabel, Veronika Zimmer, and Cosmin I. Bercea\n(https://arxiv.org/pdf/2407.15119)\n Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data\nRichard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir\n(https://arxiv.org/abs/2407.12669)\n Complex-valued Federated Learning with Differential Privacy and MRI Applications\nAnneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia Schnabel, Georgios Kaissis ([link will be available soon])\n ","date":1720137600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1720137600,"objectID":"0c23de36658fd3faddd1dd09b41f07ad","permalink":"https://compai-lab.io/post/miccai_workshops_24/","publishdate":"2024-07-05T00:00:00Z","relpermalink":"/post/miccai_workshops_24/","section":"post","summary":"Selective Test-Time Adaptation using Neural Implicit Representations for Unsupervised Anomaly Detection [Best Paper Award]\nSameer Ambekar, Julia Schnabel, and Cosmin I. Bercea. https://arxiv.org/abs/2410.03306\n MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI","tags":null,"title":"Eleven papers accepted at MICCAI Workshops 2024","type":"post"},{"authors":["Cosmin I. Bercea","Anna Reithmeir","Hannah Eichhorn","Veronika Spieker","Richard Osuala","Maxime Di Folco","Stefan Fischer"],"categories":null,"content":" Diffusion Models with Implicit Guidance for Medical Anomaly Detection\nCosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Julia A. Schnabel\n(https://arxiv.org/abs/2403.08464)\n Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI\nHannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, and Julia A. Schnabel\n(https://arxiv.org/abs/2403.08298)\n Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks\nStefan M. Fischer, Lina Felsner, Daniel M. Lang, Richard Osuala, Johannes Kiechle, Jan C. Peeken, Julia A. Schnabel\n Interpretable Representation Learning of Cardiac MRI via Attribute Regularization\nMaxime Di Folco, Cosmin I. Bercea, Emily Chan, Julia A. Schnabel\n(https://arxiv.org/abs/2406.08282)\n Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models\nRichard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia Schnabel, and Karim Lekadir\n(https://arxiv.org/abs/2403.13890)\n Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration\nAnna Reithmeir, Lina Felsner, Rickmer Braren, Julia A. Schnabel, Veronika A. Zimmer\n Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation\nVeronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rückert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel\n(https://arxiv.org/abs/2404.08350)\n ","date":1720137600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1720137600,"objectID":"b9c26a5a89f2bcee712adc652e863a6d","permalink":"https://compai-lab.io/post/miccai_24/","publishdate":"2024-07-05T00:00:00Z","relpermalink":"/post/miccai_24/","section":"post","summary":"Diffusion Models with Implicit Guidance for Medical Anomaly Detection\nCosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Julia A. Schnabel\n(https://arxiv.org/abs/2403.08464)\n Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI","tags":null,"title":"Seven papers accepted at MICCAI 2024","type":"post"},{"authors":["Stefan Fischer","Johannes Kiechle","Daniel M. Lang"],"categories":null,"content":"Stefan M. Fischer’s submission to the MICCAI2023 Lymph Node Quantification Challenge won the 3rd price.\nTherefore, the challenge team was invited for a presentation at MICCAI 2023 and to a Special Issue Submission at the MELBA Journal. The journal submission “Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge” is now available at MELBA.\nThe paper is available here.\n","date":1718323200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1718323200,"objectID":"57f03682f3333bd5067ebd63faf63ae8","permalink":"https://compai-lab.io/post/fischer_melba_24/","publishdate":"2024-06-14T00:00:00Z","relpermalink":"/post/fischer_melba_24/","section":"post","summary":"Stefan M. Fischer’s submission to the MICCAI2023 Lymph Node Quantification Challenge won the 3rd price.\nTherefore, the challenge team was invited for a presentation at MICCAI 2023 and to a Special Issue Submission at the MELBA Journal.","tags":null,"title":"Paper Accepted at MELBA Journal","type":"post"},{"authors":["Hannah Eichhorn"],"categories":null,"content":"Hannah Eichhorn has been elected as Trainee Representative of the ISMRM Motion Detection \u0026amp; Correction Study Group. She started her term at the ISMRM Annual Meeting in Singapore in the beginning of May.\nThe Study Group’s mission is to investigate how various forms of motion can affect MR data, how motion can be detected, how to deal best with motion-corrupted data, and what can be done to prevent MR data from getting corrupted by motion.\n","date":1716422400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1716422400,"objectID":"1541697da02a7932e10e891e3b2b44a0","permalink":"https://compai-lab.io/post/eichhorn_study_group_5_24/","publishdate":"2024-05-23T00:00:00Z","relpermalink":"/post/eichhorn_study_group_5_24/","section":"post","summary":"Hannah Eichhorn has been elected as Trainee Representative of the ISMRM Motion Detection \u0026 Correction Study Group. She started her term at the ISMRM Annual Meeting in Singapore in the beginning of May.","tags":null,"title":"Hannah Eichhorn elected as ISMRM Study Group Trainee Representative","type":"post"},{"authors":["Julia A. Schnabel"],"categories":null,"content":"The Alfred Breit Prize 2024 of the Radiological Society was awarded to Prof. Julia Schnabel, Professor at the Technical University of Munich and Director at the Institute of Machine Learning in Biomedical Imaging at Helmholtz Munich. The prize honors outstanding work in the research of radio-oncology.\nMore information here and here.\n","date":1715299200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1715299200,"objectID":"580c186f663be912d18931632bc998c0","permalink":"https://compai-lab.io/post/schnabel_alfred_breit_preis_24/","publishdate":"2024-05-10T00:00:00Z","relpermalink":"/post/schnabel_alfred_breit_preis_24/","section":"post","summary":"The Alfred Breit Prize 2024 of the Radiological Society was awarded to Prof. Julia Schnabel, Professor at the Technical University of Munich and Director at the Institute of Machine Learning in Biomedical Imaging at Helmholtz Munich.","tags":null,"title":"German Radiological Society Awards the Alfred Breit Prize to Prof. Julia Schnabel","type":"post"},{"authors":["Anna Reithmeir"],"categories":null,"content":"Anna Reithmeir’s paper ‘Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks’ was accepted at SPIE Medical Imaging 2024 which was held 18-22 Feb. 2024 in San Diego, US.\nThe paper is among the finalists for the best student paper award.\n","date":1710892800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1710892800,"objectID":"d0b246f450372ee4221dab7aa28f914c","permalink":"https://compai-lab.io/post/reithmeir_spie_24/","publishdate":"2024-03-20T00:00:00Z","relpermalink":"/post/reithmeir_spie_24/","section":"post","summary":"Anna Reithmeir’s paper ‘Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks’ was accepted at SPIE Medical Imaging 2024 which was held 18-22 Feb. 2024 in San Diego, US.\nThe paper is among the finalists for the best student paper award.","tags":null,"title":"Paper accepted at SPIE Medical Imaging 2024 and Finalist of Best Student Paper Award","type":"post"},{"authors":["Johannes Kiechle"],"categories":null,"content":"Johannes Kiechle’s paper has been accepted to be presented at International Symposium on Biomedical Imaging 2024 Annual Meeting in Athens.\nJohannes Kiechle will present his work “Unifying Local and Global Shape Descriptors to Grade Soft-Tissue Sarcomas using Graph Convolutional Networks”.\n","date":1710460800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1710460800,"objectID":"2eb85c42856d122059d4364f549ac0f9","permalink":"https://compai-lab.io/post/kiechle_isbi_24/","publishdate":"2024-03-15T00:00:00Z","relpermalink":"/post/kiechle_isbi_24/","section":"post","summary":"Johannes Kiechle’s paper has been accepted to be presented at International Symposium on Biomedical Imaging 2024 Annual Meeting in Athens.\nJohannes Kiechle will present his work “Unifying Local and Global Shape Descriptors to Grade Soft-Tissue Sarcomas using Graph Convolutional Networks”.","tags":null,"title":"Paper accepted at ISBI 2024","type":"post"},{"authors":null,"categories":null,"content":"Course details\nTransfer learning enables the effective utilization of knowledge gained from one task or domain to enhance performance in another, while domain adaptation focuses on adapting models trained on a particular domain to perform well in related but different domains. This seminar looks at the concepts of transfer learning and domain adaptation in general and with the application in medical imaging. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed.\nKey topics to be covered include:\n Introduction to transfer learning and domain adaptation Implications in the context of medical imaging Examples of transfer learning and domain adaptation in medical imaging State-of-the-art methods Clinical applications Requirements:\n Background in image processing and machine learning/deep learning Interest in medical image analysis Interest in research Please register via the TUM matching system: https://matching.in.tum.de\nCheck the intro slides here: ","date":1710460800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1710460800,"objectID":"e818923d2066008177b7b1499402f548","permalink":"https://compai-lab.io/teaching/domain_adaptation_seminar/","publishdate":"2024-03-15T00:00:00Z","relpermalink":"/teaching/domain_adaptation_seminar/","section":"teaching","summary":"Summer semester 2024. TUM Informatics. Seminar.","tags":["ss24"],"title":"Transfer Learning and Domain Adaptation in Medical Imaging (IN0014, IN2107)","type":"teaching"},{"authors":["Johannes Kiechle"],"categories":null,"content":"Johannes Kiechle’s abstract has been accepted to be presented as an oral at The European SocieTy for Radiotherapy and Oncology (ESTRO) 2024 Annual Meeting in Glasgow.\nJohannes Kiechle will present his work “Investigating the role of morphology in deep learning-based liposarcoma grading” on Monday, 06 May 2024.\n","date":1710374400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1710374400,"objectID":"663639f6a2cd78d682f1eb51f788229e","permalink":"https://compai-lab.io/post/kiechle_estro_24/","publishdate":"2024-03-14T00:00:00Z","relpermalink":"/post/kiechle_estro_24/","section":"post","summary":"Johannes Kiechle’s abstract has been accepted to be presented as an oral at The European SocieTy for Radiotherapy and Oncology (ESTRO) 2024 Annual Meeting in Glasgow.\nJohannes Kiechle will present his work “Investigating the role of morphology in deep learning-based liposarcoma grading” on Monday, 06 May 2024.","tags":null,"title":"Abstract accepted at ESTRO 2024 (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 orals at the 2024 ISMRM \u0026amp; ISMRT Annual Meeting.\nHannah Eichhorn will present her work “PHIMO: Physics-Informed Motion Correction of GRE MRI for T2 Quantification*” on Tuesday, 07 May 2024 at 8:15 am SGT. Check this GitHub repository for more information.\nVeronika Spieker will present her work “DE-NIK: Leveraging Dual-Echo Data for Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit k-Space Representations” on Monday, 06 May 2024 at 8:15 am SGT. Check this GitHub repository for more information.\n","date":1706745600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1706745600,"objectID":"83844ca2fc30ca519b0cd3ac5ca443dc","permalink":"https://compai-lab.io/post/spieker_eichhorn_ismrm24/","publishdate":"2024-02-01T00:00:00Z","relpermalink":"/post/spieker_eichhorn_ismrm24/","section":"post","summary":"Veronika Spieker’s and Hannah Eichhorn’s abstracts have been accepted to be presented as orals at the 2024 ISMRM \u0026 ISMRT Annual Meeting.\nHannah Eichhorn will present her work “PHIMO: Physics-Informed Motion Correction of GRE MRI for T2 Quantification*” on Tuesday, 07 May 2024 at 8:15 am SGT.","tags":null,"title":"Two abstracts accepted at 2024 ISMRM \u0026 ISMRT Annual Meeting (oral talks)","type":"post"},{"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.\n 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.\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":["Julia A. Schnabel"],"categories":null,"content":"Interview with Prof. Julia Schnabel by Süddeutsche Zeitung about artificial intelligence in clinical practice. Available online here\n","date":1692748800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1692748800,"objectID":"256e2d5644726692baa59dfd2705e581","permalink":"https://compai-lab.io/post/schnabel_sueddeutsche_23/","publishdate":"2023-08-23T00:00:00Z","relpermalink":"/post/schnabel_sueddeutsche_23/","section":"post","summary":"Interview with Prof. Julia Schnabel by Süddeutsche Zeitung about artificial intelligence in clinical practice. Available online here","tags":null,"title":"Süddeutsche Zeitung Interview with Prof. Julia Schnabel","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":["ws23"],"title":"Learning of and on manifolds in medical imaging (IN2107)","type":"teaching"},{"authors":["Cosmin I. Bercea"],"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":["ws23"],"title":"Unsupervised Anomaly Detection in Medical Imaging","type":"teaching"},{"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":["ss22"],"title":"Medical Image Registation I (IN2107)","type":"teaching"},{"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":["ws21"],"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":["ss22"],"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. 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\ No newline at end of file
diff --git a/index.xml b/index.xml
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+++ b/index.xml
@@ -0,0 +1,955 @@
+
+
+
+ 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’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>
+
+
+
+
+ Latent Functional Maps for Medical Imaging
+ https://compai-lab.io/vacancies/msc_functionalmaps/
+ Tue, 13 Aug 2024 00:00:00 +0000
+ https://compai-lab.io/vacancies/msc_functionalmaps/
+ <p>Abstract:</p>
+<p>Neural Networks (NNs) learn to represent high-dimensional data as elements of lower-dimensional latent spaces. Modeling the relationships between these representational spaces is an ongoing challenge. Successfully addressing this challenge could enable the reuse of representations in downstream tasks, reducing the need to retrain similar models multiple times. Recently, Fumero et al. leveraged the internal geometry of representations and proposed applying latent functional maps to align representations across distinct models, demonstrating its relevance for comparing representations. However, these kinds of approaches have not yet been explored in the context of medical imaging datasets, where aligning multimodal representa-
+tions could significantly enhance the effectiveness of models in medical applications. This project aims to use latent functional maps to align multimodal medical representations (e.g., text and vision). The first part of the thesis will involve a literature review on representation similarity. This will be followed by experimenting with the latent functional maps approach on a toy dataset of medical images and later applying it to real medical imaging tasks.</p>
+
+
+
+
+ AI for Vision-Language Models in Medical Imaging (IN2107)
+ https://compai-lab.io/teaching/vlm_seminar/
+ Thu, 25 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/teaching/vlm_seminar/
+ <p>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+<figure >
+ <div class="d-flex justify-content-center">
+ <div class="w-100" ><img src="https://compai-lab.io/images/vlm_teaser.gif" alt="Teaser" loading="lazy" data-zoomable /></div>
+ </div></figure>
+</p>
+<p><strong>Time</strong>: Wednesday 14-16.</p>
+<p><strong>Location</strong>: - Garching (in-person): FMI, 5610.01.11 <a href="https://nav.tum.de/room/5610.01.011" target="_blank" rel="noopener">https://nav.tum.de/room/5610.01.011</a></p>
+<ul>
+<li>some invited talks on Zoom: <a href="https://tum-conf.zoom-x.de/my/cibercea?pwd=WlMvanU1NUcveUtjVTJrWHAzWFp1dz09" target="_blank" rel="noopener">https://tum-conf.zoom-x.de/my/cibercea?pwd=WlMvanU1NUcveUtjVTJrWHAzWFp1dz09</a></li>
+</ul>
+<p>Vision-language models (VLMs) in medical imaging leverage the integration of visual data and textual information to enhance representation learning. These models can be pre-trained to improve representations, enabling a wide range of downstream applications. This seminar will explore foundational concepts, current methodologies, and recent advancements in applying vision-language models to diverse tasks in medical imaging, such as:</p>
+<ul>
+<li>Synthetic image synthesis</li>
+<li>Anomaly detection</li>
+<li>Clinical report generation</li>
+<li>Visual-question answering</li>
+<li>Classification</li>
+<li>Segmentation</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> or write an e-mail to <a href="mailto:cosmin.bercea@tum.de">cosmin.bercea@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/VLM_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div>
+ </div></figure>
+</p>
+<object data="/files/VLM_seminar.pdf" type="application/pdf" width="100%" height="400">
+</object>
+
+
+
+
+ Master Seminar - Medical Image Registration (IN2107, IN4462)
+ https://compai-lab.io/old_stuff/teaching/registration_seminar_ws24/
+ Thu, 25 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/old_stuff/teaching/registration_seminar_ws24/
+ <p><strong>Time</strong>: Wednesday 10-12 a.m.</p>
+<p><strong>Location</strong>: Garching (in-person)</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>
+<p>Requirements:</p>
+<ul>
+<li>Background in image processing and machine learning</li>
+<li>Interest in medical image analysis</li>
+</ul>
+<p>Goal and organization:</p>
+<p>The participating students will learn the fundamental concepts of image registration. They will acquire the skills to analyze critically state-of-the-art research work and to define own research questions. Basic concepts will be introduced with an overview of different research topics.
+The participants will select a research paper (suggestions given by the lecturers) and independently work on it with a final oral presentation and a written report.
+Presentations of members of international research groups will provide the students with insights into state-of-the-art research in the field.</p>
+<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> or write an email to <a href="mailto:anna.reithmeir@tum.de">anna.reithmeir@tum.de</a>.</p>
+<p>The seminar will take place Wednesdays from 10 a.m. to 12.a.m. in Garching.</p>
+
+
+
+
+ Temporal Landmark Tracking on Medical Imaging
+ https://compai-lab.io/old_stuff/teaching/msc_tracking/
+ Thu, 25 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/old_stuff/teaching/msc_tracking/
+ <p>Abstract:</p>
+<p>Even though various learning-based computer vision methods have been developed for pixel tracking, motion estimation in video data depicts a challenging task. Part of the problem arises from the 3D-to-2D projection process that can lead to out-of-plane motion, which impedes long-range pixel trajectory estimation. In the medical domain, video data, i.e. fast magnetic resonance imaging (MRI) sequences, can be used for guidance during treatment. Specifically, in radiation therapy, contouring algorithms are used for tracking of the target volume supposed to receive the main radiation dose during treatment. Delineation can, for example, be performed with a U-Net architecture. However, such an approach only allows for identification of larger structures, while irregular movement can be subtle and localized. Landmark detection models are able to identify such localized regions between different representations of the same object. Furthermore, they are faster than semantic segmentation models, and therefore, allow for computer aided intervention during treatment. In this thesis, different state-of-the-art landmark and pixel tracking algorithms will be tested and further enhanced to identify movement on temporal imaging data of the lungs, i.e. 4D CT. Furthermore, ability of such landmarks to identify movement differing from a normal state, i.e. allowing for identification of anomalies, will be studied.</p>
+
+
+
+
+ Eleven papers accepted at MICCAI Workshops 2024
+ https://compai-lab.io/post/miccai_workshops_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_workshops_24/
+ <ul>
+<li>
+<p><strong>Selective Test-Time Adaptation using Neural Implicit Representations for Unsupervised Anomaly Detection [Best Paper Award]</strong><br>
+Sameer Ambekar, Julia Schnabel, and Cosmin I. Bercea. <br>
+<a href="https://arxiv.org/abs/2410.03306" target="_blank" rel="noopener">https://arxiv.org/abs/2410.03306</a><br/><br/></p>
+</li>
+<li>
+<p><strong>MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI</strong><br>
+Malek Ben Alaya, Daniel M. Lang, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15270" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15270</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Unsupervised Analysis of Alzheimer’s Disease Signatures using 3D Deformable Autoencoders</strong><br>
+Mehmet Yigit Avci, Emily Chan, Veronika Zimmer, Daniel Rueckert, Benedikt Wiestler, Julia A. Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.03863" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.03863</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion Models</strong><br>
+Deniz Daum; Richard Osuala; Anneliese Riess; Georgios Kaissis; Julia A. Schnabel; Maxime Di Folco<br>
+(<a href="https://arxiv.org/abs/2407.16405" target="_blank" rel="noopener">https://arxiv.org/abs/2407.16405</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Graph Neural Networks: A suitable alternative to MLPs in latent 3D medical image classification?</strong><br>
+Johannes Kiechle, Daniel M. Lang, Stefan M. Fischer, Lina Felsner, Jan C. Peeken, Julia A. Schnabel<br>
+(<a href="http://arxiv.org/abs/2407.17219" target="_blank" rel="noopener">http://arxiv.org/abs/2407.17219</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>General Vision Encoder Features as Guidance in Medical Image Registration</strong><br>
+Fryderyk Kögl, Anna Reithmeir, Vasiliki Sideri-Lampretsa, Ines Machado, Rickmer Braren, Daniel Rückert, Julia A Schnabel, Veronika A Zimmer<br>
+(<a href="https://arxiv.org/abs/2407.13311" target="_blank" rel="noopener">https://arxiv.org/abs/2407.13311</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Language Models Meet Anomaly Detection for Better Interpretability and Generalizability</strong><br>
+Jun Li, Su Hwan Kim, Philip Müller, Lina Felsner, Daniel Rueckert, Benedikt Wiestler, Julia A.Schnabel, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2404.07622v2" target="_blank" rel="noopener">https://arxiv.org/pdf/2404.07622v2</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>A Self-Supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer</strong><br>
+Inês P. Machado, Anna Reithmeir, Fryderyk Kogl, Leonardo Rundo, Gabriel Funingana, Marika Reinius, Gift Mungmeeprued, Zeyu Gao, Cathal McCague, Eric Kerfoot, Ramona Woitek, Evis Sala, Yangming Ou, James Brenton, Julia Schnabel, Mireia Crispin<br>
+(<a href="https://arxiv.org/abs/2407.17114" target="_blank" rel="noopener">https://arxiv.org/abs/2407.17114</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound</strong><br>
+Hanna Mykula, Lisa Gasser, Silvia Lobmaier, Julia A. Schnabel, Veronika Zimmer, and Cosmin I. Bercea<br>
+(<a href="https://arxiv.org/pdf/2407.15119" target="_blank" rel="noopener">https://arxiv.org/pdf/2407.15119</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data</strong><br>
+Richard Osuala, Daniel M. Lang, Anneliese Riess, Georgios Kaissis, Zuzanna Szafranowska, Grzegorz Skorupko, Oliver Diaz, Julia A. Schnabel, Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2407.12669" target="_blank" rel="noopener">https://arxiv.org/abs/2407.12669</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Complex-valued Federated Learning with Differential Privacy and MRI Applications</strong><br>
+Anneliese Riess, Alexander Ziller, Stefan Kolek, Daniel Rueckert, Julia Schnabel, Georgios Kaissis <br>
+([link will be available soon])<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ Seven papers accepted at MICCAI 2024
+ https://compai-lab.io/post/miccai_24/
+ Fri, 05 Jul 2024 00:00:00 +0000
+ https://compai-lab.io/post/miccai_24/
+ <ul>
+<li>
+<p><strong>Diffusion Models with Implicit Guidance for Medical Anomaly Detection</strong><br>
+Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08464" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08464</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Physics-Informed Deep Learning for Motion-Corrected Reconstruction of Quantitative Brain MRI</strong><br>
+Hannah Eichhorn, Veronika Spieker, Kerstin Hammernik, Elisa Saks, Kilian Weiss, Christine Preibisch, and Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2403.08298" target="_blank" rel="noopener">https://arxiv.org/abs/2403.08298</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks</strong><br>
+Stefan M. Fischer, Lina Felsner, Daniel M. Lang, Richard Osuala, Johannes Kiechle, Jan C. Peeken, Julia A. Schnabel<br/><br/></p>
+</li>
+<li>
+<p><strong>Interpretable Representation Learning of Cardiac MRI via Attribute Regularization</strong><br>
+Maxime Di Folco, Cosmin I. Bercea, Emily Chan, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2406.08282" target="_blank" rel="noopener">https://arxiv.org/abs/2406.08282</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Towards Learning Contrast Kinetics with Multi-Condition Latent Diffusion Models</strong><br>
+Richard Osuala, Daniel M. Lang, Preeti Verma, Smriti Joshi, Apostolia Tsirikoglou, Grzegorz Skorupko, Kaisar Kushibar, Lidia Garrucho, Walter H. L. Pinaya, Oliver Diaz, Julia Schnabel, and Karim Lekadir<br>
+(<a href="https://arxiv.org/abs/2403.13890" target="_blank" rel="noopener">https://arxiv.org/abs/2403.13890</a>)<br/><br/></p>
+</li>
+<li>
+<p><strong>Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration</strong><br>
+Anna Reithmeir, Lina Felsner, Rickmer Braren, Julia A. Schnabel, Veronika A. Zimmer<br/><br/></p>
+</li>
+<li>
+<p><strong>Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representation</strong><br>
+Veronika Spieker, Hannah Eichhorn, Jonathan K. Stelter, Wenqi Huang, Rickmer F. Braren, Daniel Rückert, Francisco Sahli Costabal, Kerstin Hammernik, Claudia Prieto, Dimitrios C. Karampinos, Julia A. Schnabel<br>
+(<a href="https://arxiv.org/abs/2404.08350" target="_blank" rel="noopener">https://arxiv.org/abs/2404.08350</a>)<br/><br/></p>
+</li>
+</ul>
+
+
+
+
+ Paper Accepted at MELBA Journal
+ https://compai-lab.io/post/fischer_melba_24/
+ Fri, 14 Jun 2024 00:00:00 +0000
+ https://compai-lab.io/post/fischer_melba_24/
+ <p>Stefan M. Fischer’s submission to the MICCAI2023 Lymph Node Quantification Challenge won the 3rd price.<br>
+Therefore, the challenge team was invited for a presentation at MICCAI 2023 and to a Special Issue Submission at the MELBA Journal.
+The journal submission “<em>Mask the Unknown: Assessing Different Strategies to Handle Weak Annotations in the MICCAI2023 Mediastinal Lymph Node Quantification Challenge</em>” is now available at MELBA.<br>
+The paper is available <a href="https://www.melba-journal.org/papers/2024:008.html" target="_blank" rel="noopener">here</a>.</p>
+
+
+
+
+ Hannah Eichhorn elected as ISMRM Study Group Trainee Representative
+ https://compai-lab.io/post/eichhorn_study_group_5_24/
+ Thu, 23 May 2024 00:00:00 +0000
+ https://compai-lab.io/post/eichhorn_study_group_5_24/
+ <p>Hannah Eichhorn has been elected as Trainee Representative of the ISMRM Motion Detection & Correction Study Group. She started her term at the ISMRM Annual Meeting in Singapore in the beginning of May.</p>
+<p>The Study Group’s mission is to investigate how various forms of motion can affect MR data, how motion can be detected, how to deal best with motion-corrupted data, and what can be done to prevent MR data from getting corrupted by motion.</p>
+
+
+
+
+ German Radiological Society Awards the Alfred Breit Prize to Prof. Julia Schnabel
+ https://compai-lab.io/post/schnabel_alfred_breit_preis_24/
+ Fri, 10 May 2024 00:00:00 +0000
+ https://compai-lab.io/post/schnabel_alfred_breit_preis_24/
+ <p>The Alfred Breit Prize 2024 of the Radiological Society was awarded to Prof. Julia Schnabel, Professor at the Technical University of Munich and Director at the Institute of Machine Learning in Biomedical Imaging at Helmholtz Munich. The prize honors outstanding work in the research of radio-oncology.</p>
+<p>More information <a href="https://www.drg.de/de-DE/10884/zweifache-ehrung-drg-verleiht-alfred-breit-preis-an-prof-dr-julia-schnabel-aus-muenchen-und-prof-dr-norbert-hosten-aus-greifswald/" target="_blank" rel="noopener">here</a> and <a href="https://www.helmholtz-munich.de/newsroom/news/artikel/deutsche-roentgengesellschaft-verleiht-alfred-breit-preis-an-prof-julia-schnabel" target="_blank" rel="noopener">here</a>.</p>
+
+
+
+
+ Paper accepted at SPIE Medical Imaging 2024 and Finalist of Best Student Paper Award
+ https://compai-lab.io/post/reithmeir_spie_24/
+ Wed, 20 Mar 2024 00:00:00 +0000
+ https://compai-lab.io/post/reithmeir_spie_24/
+ <p>Anna Reithmeir’s paper ‘Learning Physics-Inspired Regularization for Medical Image Registration with Hypernetworks’ was accepted at SPIE Medical Imaging 2024 which was held 18-22 Feb. 2024 in San Diego, US.</p>
+<p>The paper is among the finalists for the best student paper award.</p>
+
+
+
+
+ Paper accepted at ISBI 2024
+ https://compai-lab.io/post/kiechle_isbi_24/
+ Fri, 15 Mar 2024 00:00:00 +0000
+ https://compai-lab.io/post/kiechle_isbi_24/
+ <p>Johannes Kiechle’s paper has been accepted to be presented at International Symposium on Biomedical Imaging 2024 Annual Meeting in Athens.</p>
+<p>Johannes Kiechle will present his work “<em>Unifying Local and Global Shape Descriptors to Grade Soft-Tissue Sarcomas using Graph Convolutional Networks</em>”.</p>
+
+
+
+
+ Transfer Learning and Domain Adaptation in Medical Imaging (IN0014, IN2107)
+ https://compai-lab.io/teaching/domain_adaptation_seminar/
+ Fri, 15 Mar 2024 00:00:00 +0000
+ https://compai-lab.io/teaching/domain_adaptation_seminar/
+ <p><a href="https://campus.tum.de/tumonline/ee/ui/ca2/app/desktop/#/slc.tm.cp/student/courses/950769202?$scrollTo=toc_overview" target="_blank" rel="noopener">Course details</a></p>
+<p>Transfer learning enables the effective utilization of knowledge gained from one task or domain to enhance performance in another, while domain adaptation focuses on adapting models trained on a particular domain to perform well in related but different domains.
+This seminar looks at the concepts of transfer learning and domain adaptation in general and with the application in medical imaging. Selected material of methods and applications from the field of medical imaging will be covered. Basic problem formulations to recent advances will be discussed.</p>
+<p>Key topics to be covered include:</p>
+<ul>
+<li>Introduction to transfer learning and domain adaptation</li>
+<li>Implications in the context of medical imaging</li>
+<li>Examples of transfer learning and domain adaptation in medical imaging</li>
+<li>State-of-the-art methods</li>
+<li>Clinical applications</li>
+</ul>
+<p>Requirements:</p>
+<ul>
+<li>Background in image processing and machine learning/deep learning</li>
+<li>Interest in medical image analysis</li>
+<li>Interest in research</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/slides_domain_adaptation_seminar.pdf" alt="Slides" loading="lazy" data-zoomable /></div>
+ </div></figure>
+</p>
+<object data="/files/slides_domain_adaptation_seminar.pdf" type="application/pdf" width="100%" height="400">
+</object>
+
+
+
+
+ Abstract accepted at ESTRO 2024 (oral talk)
+ https://compai-lab.io/post/kiechle_estro_24/
+ Thu, 14 Mar 2024 00:00:00 +0000
+ https://compai-lab.io/post/kiechle_estro_24/
+ <p>Johannes Kiechle’s abstract has been accepted to be presented as an oral at The European SocieTy for Radiotherapy and Oncology (ESTRO) 2024 Annual Meeting in Glasgow.</p>
+<p>Johannes Kiechle will present his work “<em>Investigating the role of morphology in deep learning-based liposarcoma grading</em>” on Monday, 06 May 2024.</p>
+
+
+
+
+ Two abstracts accepted at 2024 ISMRM & ISMRT Annual Meeting (oral talks)
+ https://compai-lab.io/post/spieker_eichhorn_ismrm24/
+ Thu, 01 Feb 2024 00:00:00 +0000
+ https://compai-lab.io/post/spieker_eichhorn_ismrm24/
+ <p>Veronika Spieker’s and Hannah Eichhorn’s abstracts have been accepted to be presented as orals at the 2024 ISMRM & ISMRT Annual Meeting.</p>
+<p>Hannah Eichhorn will present her work “<em>PHIMO: Physics-Informed Motion Correction of GRE MRI for T2</em> Quantification*” on Tuesday, 07 May 2024 at 8:15 am SGT. Check <a href="https://github.com/HannahEichhorn/PHIMO" target="_blank" rel="noopener">this GitHub repository</a> for more information.</p>
+<p>Veronika Spieker will present her work “<em>DE-NIK: Leveraging Dual-Echo Data for Respiratory-Resolved Abdominal MR Reconstructions Using Neural Implicit k-Space Representations</em>” on Monday, 06 May 2024 at 8:15 am SGT. Check <a href="https://github.com/vjspi/DE-NIK" target="_blank" rel="noopener">this GitHub repository</a> for more information.</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>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+<figure >
+ <div class="d-flex justify-content-center">
+ <div class="w-100" ><img alt="img" srcset="
+ /post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_e1ff7f723fc5ed308be173642a5f92f5.webp 400w,
+ /post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_59a22aa363f30bc9c49ab63c04f6c200.webp 760w,
+ /post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_1200x1200_fit_q75_h2_lanczos_3.webp 1200w"
+ src="https://compai-lab.io/post/spieker_eichhorn_tmi/img_hu97b0dcc97f3d04d523dba4b92347ab90_2209044_e1ff7f723fc5ed308be173642a5f92f5.webp"
+ width="760"
+ height="713"
+ loading="lazy" data-zoomable /></div>
+ </div></figure>
+</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 “Sparse annotation strategies for segmentation of short axis cardiac MRI” (<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>
+
+
+
+ Süddeutsche Zeitung Interview with Prof. Julia Schnabel
+ https://compai-lab.io/post/schnabel_sueddeutsche_23/
+ Wed, 23 Aug 2023 00:00:00 +0000
+ https://compai-lab.io/post/schnabel_sueddeutsche_23/
+ <p>Interview with Prof. Julia Schnabel by Süddeutsche Zeitung about artificial intelligence in clinical practice. Available online <a href="https://www.sueddeutsche.de/kultur/kuenstliche-intelligenz-medizin-gesundheitsversorgung-1.6074505?reduced=true" target="_blank" rel="noopener">here</a></p>
+
+
+
+
+ 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>
+
+
+
+
+ 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>“<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>“<em>Generalizing Unsupervised Anomaly Detection: Towards Unbiased Pathology Screening</em>” 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’s and Hannah Eichhorn’s abstracts have been accepted to be presented as digital posters at the 2023 ISMRM & ISMRT Annual Meeting.</p>
+<p>Veronika Spieker will present her work on “<em>Patient-specific respiratory liver motion analysis for individual motion-resolved reconstruction</em>” on Monday, 05 June 2023 at 1:45 pm EDT.</p>
+<p>Hannah Eichhorn will present her work on “<em>Investigating the Impact of Motion and Associated B0 Changes on Oxygenation Sensitive MRI through Realistic Simulations</em>” 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’s and master’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>
+
+
+
+
+ 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&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 & 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</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
+ https://compai-lab.io/fpublications/schnabel-2001-generic/
+
+
+
+
+
+ https://compai-lab.io/admin/config.yml
+ Mon, 01 Jan 0001 00:00:00 +0000
+ https://compai-lab.io/admin/config.yml
+
+
+
+
+
+ https://compai-lab.io/contact/
+ Mon, 01 Jan 0001 00:00:00 +0000
+ https://compai-lab.io/contact/
+
+
+
+
+ Tour
+ https://compai-lab.io/tour/
+ Mon, 01 Jan 0001 00:00:00 +0000
+ https://compai-lab.io/tour/
+
+
+
+
+
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new file mode 100644
index 0000000..7a10623
--- /dev/null
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