Updated datasets 2024-08-02 UTC

datasets/bobsrepository.yaml

@@ -1,8 +1,8 @@
 Name: Baby Open Brains (BOBs) Repository on AWS
 Description: "Manually curated and reviewed infant brain segmentations and accompanying T1w and T2w images for a range of 1-9 month old participants from the Baby Connectome Project (BCP)"
-Documentation: https://osf.io/wdr78/
+Documentation: https://bobsrepository.readthedocs.io/en/latest/
 Contact: Eric Feczko ([email protected]) & Sally M. Stoyell ([email protected])
-ManagedBy: Center for Developmental Neuroscience (CDNI), "[Masonic Institute for the Developing Brain (MIDB)](https://midb.umn.edu/)"
+ManagedBy: "Masonic Institute for the Developing Brain (MIDB) Open Data Initiative"
 UpdateFrequency: "The repository is updated when: (1) all brain segmentations have undergone further rounds of manual correction, which may include refinement of existing ROIs and/or delineation of additional FreeSurfer ROIs and/or (2) new manually refined data from different age ranges and/or source studies becomes available"
 Tags:
   - neuroimaging
@@ -19,11 +19,11 @@ Resources:
     Region: us-east-2
     Type: S3 Bucket
     Explore:
-    - '[Browse Bucket](https://s3.amazonaws.com/bobsrepository)'
+    - '[Browse Bucket](https://s3.amazonaws.com/bobsrepository/index.html)'
 DataAtWork:
   Tutorials:
     - Title: Downloading BOBs Repository Data from Amazon S3 Bucket
-      URL: https://github.com/DCAN-Labs/bobsrepo/blob/main/bobsrepository_AWS_tutorial.md
+      URL: https://bobsrepository.readthedocs.io/en/latest/data_access/#how-to-download
       AuthorName: Lucille A. Moore
       AuthorURL: https://github.com/LuciMoore
   Tools & Applications:

datasets/rsna-abdominal-trauma-detection.yaml

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+Name:  RSNA Abdominal Trauma Detection (RSNA-ABT)
+Description: "Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting. To create the ground truth dataset, RSNA collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdominal injuries and a roughly equal number of cases without injury."
+Documentation: https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Abdominal-Trauma-Detection
+Contact: [email protected]
+ManagedBy: 'Radiological Society of North America (https://www.rsna.org/)'
+UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis.
+Tags:
+  - aws-pds
+  - radiology
+  - medical imaging
+  - medical image computing
+  - machine learning
+  - computer vision
+  - csv
+  - labeled
+  - computed tomography
+  - x-ray tomography
+License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3."
+Resources:
+  - Description: Zip archive containing DCM and CSV files
+    ARN: arn:aws:s3:::abdominal-trauma-detection
+    Region: us-west-2
+    Type: S3 Bucket
+    ControlledAccess: https://mira.rsna.org/dataset/5
+DataAtWork:
+  Publications:
+    - Title: The RSNA Abdominal Traumatic Injury CT (RATIC) Dataset
+      AuthorName: Rudie, Jeffrey D.
+      URL: https://doi.org/10.48550/arXiv.2405.19595

datasets/rsna-cervical-spine-fracture-detection.yaml

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+Name:  RSNA Cervical Spine Fracture Detection (RSNA-CSF) Dataset
+Description: "Over 1.5 million spine fractures occur annually in the United States alone resulting in over 17,730 spinal cord injuries annually. The most common site of spine fracture is the cervical spine. There has been a rise in the incidence of spinal fractures in the elderly and in this population, fractures can be more difficult to detect on imaging due to degenerative disease and osteoporosis. Imaging diagnosis of adult spine fractures is now almost exclusively performed with computed tomography (CT). Quickly detecting and determining the location of any vertebral fractures is essential to prevent neurologic deterioration and paralysis after trauma. RSNA has teamed with the American Society of Neuroradiology (ASNR) and the American Society of Spine Radiology (ASSR) to create this ground truth dataset, collecting imaging data from twelve sites on six continents, including approximately 2,000 CT studies. Spine radiology specialists from the ASNR and ASSR provided expert image level annotations these studies to indicate the presence, vertebral level and location of any cervical spine fractures."
+Documentation: https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Cervical-Spine-Fracture-Detection
+Contact: [email protected]
+ManagedBy: "[Radiological Society of North America](https://www.rsna.org/)"
+UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis.
+Tags:
+  - aws-pds
+  - radiology
+  - medical imaging
+  - medical image computing
+  - machine learning
+  - computer vision
+  - csv
+  - labeled
+  - computed tomography
+  - x-ray tomography
+License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3."
+Resources:
+  - Description: Zip archive containing DCM and CSV files
+    ARN: arn:aws:s3:::cervical-spine-fracture
+    Region: us-west-2
+    Type: S3 Bucket
+    ControlledAccess: https://mira.rsna.org/dataset/4
+DataAtWork:
+  Publications:
+    - Title: The RSNA Cervical Spine Fracture CT Dataset
+      URL: https://pubs.rsna.org/doi/full/10.1148/ryai.230034
+      AuthorName: Ming, Hui Lin

datasets/rsna-intracranial-hemorrhage-detection.yaml

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+Name:  RSNA Intracranial Hemorrhage Detection
+Description: "RSNA assembled this dataset in 2019 for the RSNA Intracranial Hemorrhage Detection AI Challenge (https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/). De-identified head CT studies were provided by four research institutions. A group of over 60 volunteer expert radiologists recruited by RSNA and the American Society of Neuroradiology labeled over 25,000 exams for the presence and subtype classification of acute intracranial hemorrhage."
+Documentation: https://github.com/RSNA/AI-Challenge-Data
+Contact: [email protected]
+ManagedBy: 'Radiological Society of North America (https://www.rsna.org/)'
+UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis.
+Tags:
+  - aws-pds
+  - radiology
+  - medical imaging
+  - medical image computing
+  - machine learning
+  - computer vision
+  - csv
+  - labeled
+  - computed tomography
+  - x-ray tomography
+License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3."
+Resources:
+  - Description: Zip archive containing DCM and CSV files
+    ARN: arn:aws:s3:::intracranial-hemorrhage
+    Region: us-west-2
+    Type: S3 Bucket
+    ControlledAccess: https://mira.rsna.org/dataset/2
+DataAtWork:
+  Publications:
+    - Title: "Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge"
+      URL: https://pubs.rsna.org/doi/10.1148/ryai.2020190211
+      AuthorName: Rudie, Jeffrey D.

datasets/rsna-pulmonary-embolism-detection.yaml

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+Name:  RSNA Pulmonary Embolism Detection
+Description: "RSNA assembled this dataset in 2020 for the RSNA STR Pulmonary Embolism Detection AI Challenge (https://www.kaggle.com/c/rsna-str-pulmonary-embolism-detection/). With more than 12,000 CT pulmonary angiography (CTPA) studies contributed by five international research centers, it is the largest publicly available annotated PE dataset. RSNA collaborated with the Society of Thoracic Radiology to recruit more than 80 expert thoracic radiologists who labeled the dataset with detailed clinical annotations."
+Documentation: https://github.com/RSNA/AI-Challenge-Data
+Contact: [email protected]
+ManagedBy: '[Radiological Society of North America (https://www.rsna.org/)'
+UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis.
+Tags:
+  - aws-pds
+  - radiology
+  - medical imaging
+  - medical image computing
+  - machine learning
+  - computer vision
+  - csv
+  - labeled
+  - computed tomography
+  - x-ray tomography
+License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “RSNA-STR Pulmonary Embolism CT (RSPECT) Dataset, Copyright RSNA, 2020” as follows: E Colak, FC Kitamura, SB Hobbs, et al. The RSNA Pulmonary Embolism CT Dataset [10.1148/ryai.2021200254]. Radiology: Artificial Intelligence 2021;3:2."
+Resources:
+  - Description: Zip archive containing DCM and CSV files
+    ARN: arn:aws:s3:::pulmonary-embolism-detection
+    Region: us-west-2
+    Type: S3 Bucket
+    ControlledAccess: https://mira.rsna.org/dataset/1
+DataAtWork:
+  Publications:
+    - Title: The RSNA Pulmonary Embolism CT Dataset
+      URL: https://pubs.rsna.org/doi/full/10.1148/ryai.2021200254
+      AuthorName: Colak, Errol

datasets/rsna-screening-mammography-breast-cancer-detection.yaml

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+Name:  RSNA Screening Mammography Breast Cancer Detection (RSNA-SMBC) Dataset
+Description: "According to the WHO, breast cancer is the most commonly occurring cancer worldwide. In 2020 alone, there were 2.3 million new breast cancer diagnoses and 685,000 deaths. Yet breast cancer mortality in high-income countries has dropped by 40% since the 1980s when health authorities implemented regular mammography screening in age groups considered at risk. Early detection and treatment are critical to reducing cancer fatalities, and your machine learning skills could help streamline the process radiologists use to evaluate screening mammograms. Currently, early detection of breast cancer requires the expertise of highly-trained human observers, making screening mammography programs expensive to conduct. RSNA collected screening mammograms and supporting information from two sites, totaling just under 20,000 imaging studies."
+Documentation: https://github.com/RSNA/AI-Challenge-Data/wiki/RSNA-Screening-Mammography-Breast-Cancer-Detection
+Contact: [email protected]
+ManagedBy: 'Radiological Society of North America (https://www.rsna.org/)'
+UpdateFrequency: The dataset may be updated with additional or corrected data on a need-to-update basis.
+Tags:
+  - aws-pds
+  - radiology
+  - medical imaging
+  - medical image computing
+  - machine learning
+  - computer vision
+  - csv
+  - labeled
+  - mammography
+  - breast cancer
+  - cancer
+License: "You may access and use these de-identified imaging datasets and annotations (“the data”) for non-commercial purposes only, including academic research and education, as long as you agree to abide by the following provisions: Not to make any attempt to identify or contact any individual(s) who may be the subjects of the data. If you share or re-distribute the data in any form, include a citation to the “Brain CT Hemorrhage Dataset, Copyright RSNA, 2019” as follows: Flanders AF, et al. The RSNA Brain CT Hemorrhage Dataset [10.1148/ryai.2020190211]. Radiology: Artificial Intelligence 2020;2:3."
+Resources:
+  - Description: Zip archive containing DCM and CSV files
+    ARN: arn:aws:s3:::screening-mammography-breast
+    Region: us-west-2
+    Type: S3 Bucket
+    ControlledAccess: https://mira.rsna.org/dataset/3