Skip to content Skip to navigation

Shared Datasets

Photo of chest X-ray

CheXpert: Chest X-rays

CheXpert is a dataset consisting of 224,316 chest radiographs of 65,240 patients who underwent a radiographic examination from Stanford University Medical Center between October 2002 and July 2017, in both inpatient and outpatient centers. Included are their associated radiology reports.

EchoNet-Dynamic Cardiac Ultrasound

EchoNet-Dynamic is a dataset of over 10k echocardiogram, or cardiac ultrasound, videos from unique patients at Stanford University Medical Center. Each apical-4-chamber video is accompanied by an estimated ejection fraction, end-systolic volume, end-diastolic volume, and tracings of the left ventricle performed by an advanced cardiac sonographer and reviewed by an imaging cardiologist.
Lera Ankle

LERA- Lower Extremity RAdiographs

In this retrospective, HIPAA-compliant, IRB-approved study, we collected data from 182 patients who underwent a radiographic examination at the Stanford University Medical Center between 2003 and 2014. The dataset consists of images of the foot, knee, ankle, or hip associated with each patient.

MRNet: Knee MRI's

The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction from clinical reports.


MURA (musculoskeletal radiographs) is a large dataset of bone X-rays from the Stanford University Medical Center.

Radiopaedia: List of AI Imaging Datasets

Radiopaedia has aggregated a list of public medical imaging datasets.  

RSNA: Bone Age

From the RSNA AI Challenge 2017, a dataset of bone age x-ray's from Stanford University, the University of Colorado and the University of California - Los Angeles.

RSNA: Chest Xray's

From the RSNA AI Challenge 2018, a dataset labeled chest x-rays from the the National Institutes of Health (NIH).

RSNA: CT Brain

Four research institutions provided large volumes of de-identified CT studies that were assembled to create the RSNA AI 2019 challenge dataset: Stanford University, Thomas Jefferson University, Unity Health Toronto and Universidade Federal de São Paulo (UNIFESP), The American Society of Neuroradiology (ASNR) organized a cadre of more than 60 volunteers to label over 25,000 exams for the challenge dataset.