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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.
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.
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.
CheXpert is a large chest radiograph dataset from Stanford with uncertainty-aware labels for common chest X-ray findings. It is widely used for radiology classification, label uncertainty modeling, chest X-ray representation learning, and clinical imaging benchmarks.
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.
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.
NIH Clinical Center chest X-ray dataset released for computer-aided detection and radiology machine learning research.
Stanford chest radiograph dataset for automated chest X-ray interpretation and uncertainty-aware label evaluation.
JPG-formatted chest radiographs with labels derived from free-text reports, hosted by PhysioNet.