搜索医学 AI 论文与资源

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.

ACDC is a cardiac MRI dataset for automated cardiac diagnosis and segmentation. It supports left and right ventricular segmentation, myocardium segmentation, cardiac function quantification, and evaluation of robust cardiac image analysis methods.

fastMRI is a raw MRI dataset for accelerated magnetic resonance image reconstruction, originally released by NYU Langone Health and Meta AI. It is used for MRI reconstruction, compressed sensing replacement, generative reconstruction, and robustness evaluation.

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.

C4KC-KiTS is a TCIA imaging collection associated with kidney and kidney tumor segmentation benchmarks. It supports kidney segmentation, renal tumor segmentation, surgical planning research, and evaluation of abdominal CT segmentation models.

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-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.

CMCQA is a large Chinese medical conversational question-answering dataset released with knowledge-grounded medical dialogue research. It supports medical conversation QA, knowledge-grounded response generation, and evaluation of Chinese medical dialogue systems.

MedQA is a medical examination question answering benchmark with English and Chinese medical licensing-style question sets, including mainland China and Taiwan variants. It is widely used for medical QA and medical reasoning evaluation.

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.

CBLUE is a Chinese biomedical language understanding benchmark covering real-world biomedical NLP tasks such as named entity recognition, relation extraction, term normalization, clinical trial classification, sentence similarity, and medical question answering. It is useful for evaluating Chinese clinical NLP models and medical language models.

Stanford chest radiograph dataset for automated chest X-ray interpretation and uncertainty-aware label evaluation.

Benchmark for evaluating health AI model safety, helpfulness, and clinical-relevance judgments with physician-reviewed rubrics.

Multimodal clinical data foundation and benchmark introduced at ICML 2025 for clinical foundation model research.