Resources


Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, Mehdi Moradi

The Chest ImaGenome dataset is a scene graph dataset with additional chronological comparison relations for chest X-rays. It is automatically derived from the MIMIC-CXR dataset. A manually annotated gold standard is also available for 500 patients.

multimodal radiology chest x-ray machine learning scene graph visual question answering visual dialogue object detection deep learning disease progression semantic reasoning bounding box relation extraction knowledge graph cxr chest explainability reasoning

Published: July 13, 2021. Version: 1.0.0


Database Open Access

PTB-XL, a large publicly available electrocardiography dataset

Patrick Wagner, Nils Strodthoff, Ralf-Dieter Bousseljot, Wojciech Samek, Tobias Schaeffter

The PTB-XL ECG dataset is a large dataset of 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The raw signal data has been annotated by up to two cardiologists with 71 different ECG statements and is supplemented by rich metadata.

ptb-xl ptb electrocardiography ecg

Published: April 24, 2020. Version: 1.0.1

Visualize waveforms

Database Credentialed Access

VinDr-CXR: An open dataset of chest X-rays with radiologist annotations

Ha Quy Nguyen, Hieu Huy Pham, le tuan linh, Minh Dao, lam khanh

VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations

computer vision lesion detection disease classification chest x-ray interpretation deep learning

Published: June 22, 2021. Version: 1.0.0


Challenge Open Access

Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020

Erick Andres Perez Alday, Annie Gu, Amit Shah, Chengyu Liu, Ashish Sharma, Salman Seyedi, Ali Bahrami Rad, Matthew Reyna, Gari Clifford

The goal of the 2020 PhysioNet - Computing in Cardiology Challenge is to design and implement a working, open-source algorithm that can automatically identify cardiac abnormalities in 12-lead ECG recordings.

Published: April 23, 2020. Version: 1.0.1