Resources

RadGraph is a dataset of entities and relations in full-text chest X-ray radiology reports, which are obtained using a novel information extraction (IE) schema to capture clinically relevant information in a radiology report.

Smartphone-captured CXR images including photographs taken from MIMIC-CXR and CheXpert, photographs taken by resident doctors, and photographs taken with different devices.

Wide-field calcium imaging database that consists of annotated sleep recording collected from transgenic mice at Washington University of St Louis School of Medicine.

MIMIC-III data converted into TensorFlow SequenceExample format, for use in modeling pipelines.

This is a resource for training machine learning models for language inference in the medical domain.

A curated subset of MIMIC-IV-ECHO providing apical four-chamber cine loops with manual ROI masks, volumetric labels, and ready-to-use MP4/NPZ derivatives for robust LV volume and ejection fraction research.

We present EHRXQA, the first multi-modal EHR QA dataset combining structured patient records with aligned chest X-ray images. EHRXQA contains a comprehensive set of QA pairs covering image-related, table-related, and image+table-related questions.

We introduce MIMIC-Ext-MIMIC-CXR-VQA, a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs.

Early detection of heart failure is vital for improving outcomes. The dataset contains 71,589 CXRs paired with gold standard labels from echocardiograms to enable the training of models to detect pathologies indicative of early stage heart failure.

Chest x-rays in JPG format with structured labels derived from the associated radiology report.