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The RSNA Pneumonia Detection Challenge dataset is a chest radiograph benchmark for detecting pneumonia-related lung opacities. It supports object detection, chest X-ray classification, localization, and radiology AI evaluation under a competition framework.
MURA is a musculoskeletal radiograph dataset from Stanford for abnormality detection in upper extremity X-rays. It is used for radiology classification, fracture-related screening, musculoskeletal imaging AI, and human-AI comparison studies.
CAMELYON17 is a digital pathology dataset for detecting breast cancer metastases in lymph node whole-slide images across multiple centers. It supports pathology classification, metastasis detection, weakly supervised learning, and domain generalization in histopathology AI.
The ISIC Archive is a large public dermatology image repository for skin lesion analysis. It is widely used for melanoma classification, lesion segmentation, dermoscopic image retrieval, bias and domain shift analysis, and clinical imaging benchmark development.
LIDC-IDRI is a lung CT dataset with thoracic CT scans and expert nodule annotations. It is a classic benchmark for lung nodule detection, segmentation, malignancy characterization, radiomics, and computer-aided diagnosis research.
VinDr-CXR is a chest X-ray dataset with radiologist annotations from Vietnamese hospitals. It supports abnormality classification, lesion localization, radiology object detection, and robustness studies across clinical sites and populations.
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.
PTB-XL is a large public 12-lead electrocardiography dataset with diagnostic statements and waveform records. It is a standard benchmark for ECG classification, cardiac abnormality detection, clinical signal representation learning, and robust evaluation of biosignal models.
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.
NIH Clinical Center chest X-ray dataset released for computer-aided detection and radiology machine learning research.
Foundation model and toolkit for all-in-one biomedical image parsing across recognition, detection, and segmentation tasks.