Resources


Model Credentialed Access

Shareable Artificial Intelligence to Extract Cancer Outcomes from Electronic Health Records for Precision Oncology Research

Kenneth Kehl, Pavel Trukhanov, Christopher Fong, Justin Jee, Karl Pichotta, Morgan Paul, Chelsea Nichols, Michele Waters, Nikolaus Schultz, Deborah Schrag

The DFCI-imaging-student and DFCI-medonc-student AI models for extracting cancer outcomes from imaging reports and medical oncologist notes from electronic health records.

Published: Oct. 24, 2024. Version: 1.0.0

Database Restricted Access

Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information

Alistair Johnson, Jean-Christophe Bélisle-Pipon, David Dorr, Satrajit Ghosh, Philip Payne, Maria Powell, Anais Rameau, Vardit Ravitsky, Alexandros Sigaras, Olivier Elemento, Yael Bensoussan

A dataset of voice recordings and metadata to enable the development, benchmarking, and validation of clinically applicable machine-learning models for diagnosing a wide range of health conditions.

voice bridge2ai

Published: Jan. 17, 2025. Version: 1.1

Database Restricted Access

KURIAS-ECG: a 12-lead electrocardiogram database with standardized diagnosis ontology

Hakje Yoo, Yunjin Yum, Soowan Park, Jeong Moon Lee, Moonjoung Jang, Yoojoong Kim, Jong-Ho Kim, Hyun-Joon Park, Kap Su Han, Jae Hyoung Park, Hyung Joon Joo

The KURIAS-ECG database is a high-quality 12-lead ECG DB including standard vocabulary (SNOMED CT, OMOP-CDM), and ECG diagnoses of our DB are grouped into 10 diagnoses by applying the minnesota code.

snomed minnesota 12-lead ecg

Published: Nov. 8, 2021. Version: 1.0

Database Open Access

Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX)

Amos Rubin Calamida, Farhad Nooralahzadeh, Morteza Rohanian, Mizuho Nishio, Koji Fujimoto, Michael Krauthammer

The RadEvalX is a publicly available dataset developed similarly to the ReXVal dataset. RedEvalX focuses on radiologist evaluations of errors found in automatically generated radiology reports.

Published: June 18, 2024. Version: 1.0.0

Database Credentialed Access

Chest X-ray Dataset with Lung Segmentation

Wimukthi Indeewara, Mahela Hennayake, Kasun Rathnayake, Thanuja Ambegoda, Dulani Meedeniya

CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset. This contains segmentation results of 243,324 frontal view images and corresponding masks.

segmentation medical reports u-net chest radiographs mimic-cxr chest x-ray

Published: Feb. 8, 2023. Version: 1.0.0

Database Credentialed Access

Chest X-ray segmentation images based on MIMIC-CXR

Li-Ching Chen, Po-Chih Kuo, Ryan Wang, Judy Gichoya, Leo Anthony Celi

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

segmentation chest x-rays cxr

Published: Aug. 18, 2022. Version: 1.0.0

Database Restricted Access

Smartphone-Captured Chest X-Ray Photographs

Po-Chih Kuo, ChengChe Tsai, Diego M Lopez, Alexandros Karargyris, Tom Pollard, Alistair Johnson, Leo Anthony Celi

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

smartphone photograph cxr

Published: Sept. 27, 2020. Version: 1.0.0

Challenge Credentialed Access

CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays

Gregory Holste, Mingquan Lin, Song Wang, Yiliang Zhou, Yishu Wei, Hao Chen, Atlas Wang, Yifan Peng

CXR-LT 2024 was a challenge for long-tailed, multi-label, and zero-shot thorax disease classification on chest X-rays, held at MICCAI 2024. This page contains long-tailed labels for 45 diseases from the CXR-LT 2024 and 2023 challenges.

disease classification artificial intelligence chest x-ray deep learning computer-aided diagnosis long-tailed learning cardiopulmonary disease zero-shot learning

Published: March 19, 2025. Version: 2.0.0

Database Open Access

Leipzig Heart Center ECG-Database: Arrhythmias in Children and Patients with Congenital Heart Disease

Sophia Klehs, Daniel Franke, Bayhas Alhamad, Roman Gebauer, Linus Teich, Tobias Teich, Christian Paech

This annotated ECG database for paediatric and CHD patients features 12-lead and intracardiac recordings, supporting advanced diagnostic algorithms.

artificial intelligence 12-lead ecg arrhythmias chd intracardiac recordings annotated congenital heart disease

Published: March 19, 2025. Version: 1.0.0

Visualize waveforms

Database Open Access

Brno University of Technology Smartphone PPG Database (BUT PPG)

Andrea Nemcova, Radovan Smisek, Eniko Vargova, Lucie Maršánová, Martin Vitek, Lukas Smital, Marina Filipenska, Pavlina Sikorova, Pavel Gálík

BUT PPG is a database created for the purpose of evaluating PPG signal quality and estimation of heart rate. The data comprises 3,888 10s recordings of PPGs recorded by smartphone and associated ECG and ACC signals and annotations.

heart rate artificial intelligence ppg ecg acc signal quality assessment annotations accelerometric data photoplethysmography electrocardiogram

Published: Aug. 23, 2024. Version: 2.0.0