输入关键词或点击标签，按论文、数据资源、竞赛截止日期、征稿与课程缩小范围。 标签：X-ray 范围：数据资源
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.
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.
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.
Foundation model and toolkit for all-in-one biomedical image parsing across recognition, detection, and segmentation tasks.
Google Health AI Developer Foundations open model resources for medical text and medical image understanding, including MedGemma 1.5 resources.
Interactive medical image segmentation benchmark and baseline from CVPR 2025, covering multiple modalities, organs, and target structures.