搜索医学 AI 论文与资源

AMOS is an abdominal multi-organ segmentation benchmark with CT and MRI cases for evaluating versatile medical image segmentation models. It supports abdominal organ segmentation, modality-general segmentation, and benchmarking of robust 3D segmentation methods.

REFUGE is a retinal fundus imaging challenge dataset for glaucoma assessment. It supports glaucoma classification, optic disc and cup segmentation, fovea localization, and fair comparison of ophthalmology AI methods on color fundus photographs.

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.

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.

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.

OASIS provides open-access neuroimaging datasets for studying normal aging, dementia, and brain structure. It is useful for brain MRI segmentation, age prediction, dementia classification, longitudinal modeling, and neuroimaging method benchmarking.

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.

EchoNet-Dynamic is a cardiac ultrasound video dataset with expert annotations for left ventricular function. It is used for echocardiography video understanding, ejection fraction estimation, cardiac segmentation, and clinical video AI research.

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.

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.

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.

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.

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.

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.

Sleep-EDF Expanded contains polysomnographic sleep recordings with EEG and related physiological signals. It is used for sleep stage classification, biosignal time-series modeling, self-supervised learning on physiological signals, and clinical sleep research 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.

Low-dose CT imaging collection from the National Lung Screening Trial, distributed by The Cancer Imaging Archive.

Legacy multi-task biomedical image segmentation benchmark retained as a reference; newer segmentation benchmarks are listed above it.

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.

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