Resources

A chest x-rays segmentation dataset derived from MIMIC-CXR based on deep learning algorithm and human examination.

MS-CXR is a new dataset containing 1162 chest X-ray bounding box labels paired with radiology text descriptions, annotated and verified by two board-certified radiologists.

Chest radiographs in DICOM format with associated free-text reports.

Swiss-Mammo: A physician-written, synthetic dataset of 28 German mammography reports. The dataset is stratified based on BI-RADS categories and available in German and English.

This dataset was a collected using an eye tracking system while a radiologist interpreted and read 1,083 public CXR images. The dataset contains the following aligned modalities: image, transcribed report text, dictation audio and eye gaze data.

This dataset was a collected using an eye tracking system while a radiologist interpreted and read 1,083 public CXR images. The dataset contains the following aligned modalities: image, transcribed report text, dictation audio and eye gaze data.

This dataset includes bounding box-statement pairs for chest X-ray images, derived from radiologists’ eye-tracking data (for explainability) and annotations, for local visual-language models.

The CAD-Chest dataset provides comprehensive annotations of disease, including disease severity, uncertainty, and location based on the MIMIC-CXR radiologist reports.

This dataset comprises 11,094 converted DICOM and smartphone images of Cardiac Implantable Electronic Devices (CIEDs), collected from 897 patients. It aims to facilitate the development of algorithms for CIED detection and classification.

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.