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UK Biobank Imaging provides large-scale imaging phenotypes linked to genetic, lifestyle, and health outcome data. It is used for population-scale medical imaging AI, disease risk prediction, representation learning, multimodal biomedical modeling, and epidemiological AI studies.
The Cancer Genome Atlas is a large cancer genomics resource with molecular, clinical, and pathology-related data across many cancer types. It is a foundation dataset for oncology AI, survival prediction, subtype discovery, multimodal cancer modeling, and translational biomarker research.
ADNI provides longitudinal neuroimaging, biomarker, clinical, and cognitive data for Alzheimer disease research. It supports disease progression modeling, dementia diagnosis, multimodal prediction, biomarker discovery, and clinical translation studies.
BraTS 2024 provides multimodal brain MRI data and expert annotations for brain tumor segmentation and related tumor subregion analysis. It is a major benchmark for glioma segmentation, radiology AI, and robust multimodal MRI segmentation methods.
MIMIC-IV-ECG is a large deidentified electrocardiogram dataset linked to the MIMIC-IV clinical data ecosystem. It supports ECG classification, arrhythmia detection, representation learning, and multimodal modeling with structured EHR context.
MIMIC-CXR is a large deidentified chest radiograph dataset with associated free-text radiology reports. It is widely used for chest X-ray classification, report generation, image-text representation learning, radiology retrieval, and medical multimodal foundation model evaluation.
MIMIC-IV-Note provides deidentified clinical notes linked to MIMIC-IV hospital data. It supports clinical NLP tasks such as note representation learning, discharge summary modeling, information extraction, summarization, and multimodal EHR-text modeling.
SLAKE is a semantically labeled medical visual question answering dataset with bilingual English-Chinese questions, medical images, and knowledge-enhanced annotations. It is useful for medical multimodal learning, image-grounded QA, and radiology VQA evaluation.
JPG-formatted chest radiographs with labels derived from free-text reports, hosted by PhysioNet.
Google Health AI Developer Foundations open model resources for medical text and medical image understanding, including MedGemma 1.5 resources.
Multimodal clinical data foundation and benchmark introduced at ICML 2025 for clinical foundation model research.