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AMOS is an abdominal multi-organ segmentation benchmark with CT and MRI cases for evaluating versatile medical image segmentation models. It supports abdominal organ segmentation, modality-general segmentation, and benchmarking of robust 3D segmentation methods.
OASIS provides open-access neuroimaging datasets for studying normal aging, dementia, and brain structure. It is useful for brain MRI segmentation, age prediction, dementia classification, longitudinal modeling, and neuroimaging method benchmarking.
MedMNIST v2 is a standardized collection of lightweight biomedical image classification datasets, including 2D and 3D tasks. It is useful for quick benchmarking, AutoML, foundation model sanity checks, and reproducible evaluation across multiple medical imaging domains.
NIH ChestX-ray14 is a public chest radiograph dataset with image-level labels for thoracic disease findings mined from reports. It is commonly used for chest X-ray classification, weak supervision, thoracic disease detection, and radiology benchmark comparisons.
CMB is a comprehensive Chinese medical benchmark for evaluating medical large language models on medical exams, reasoning, and clinical knowledge questions. It is suited for Chinese medical QA, LLM evaluation, and instruction-following assessment.
Deidentified EHR data for ICU and hospital patients at Beth Israel Deaconess Medical Center, distributed through PhysioNet with credentialed access.
Medical LLM benchmark and leaderboard intended to broaden coverage beyond single medical QA datasets.