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Database Open Access

VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

Hyung-Chul Lee, Chul-Woo Jung

VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

waveform anesthesia vitaldb intraoperative biosignal ecg

Published: Sept. 21, 2022. 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 21801 clinical 12-lead ECGs from 18869 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.

electrocardiography ptb-xl ptb ecg

Published: Nov. 9, 2022. Version: 1.0.3

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Database Credentialed Access

MIMIC-IV

Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, Roger Mark

Large database of de-identified health information from patients admitted to Beth Israel Deaconess Medical Center

mimic critical care machine learning intensive care unit

Published: Jan. 6, 2023. Version: 2.2

Database Credentialed Access

MIMIC-CXR Database

Alistair Johnson, Tom Pollard, Roger Mark, Seth Berkowitz, Steven Horng

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

mimic computer vision chest x-rays radiology machine learning natural language processing

Published: Sept. 19, 2019. Version: 2.0.0

Database Credentialed Access

BRAX, a Brazilian labeled chest X-ray dataset

Eduardo Pontes Reis, Joselisa Paiva, Maria Carolina Bueno da Silva, Guilherme Alberto Sousa Ribeiro, Victor Fornasiero Paiva, Lucas Bulgarelli, Henrique Lee, Paulo Victor dos Santos, vanessa brito, Lucas Amaral, Gabriel Beraldo, Jorge Nebhan Haidar Filho, Gustavo Teles, Gilberto Szarf, Tom Pollard, Alistair Johnson, Leo Anthony Celi, Edson Amaro

BRAX contains 24,959 chest radiography exams and 40,967 images acquired in a large general Brazilian hospital. All images have been read by trained radiologists and 14 labels were derived from Brazilian Portuguese reports using NLP.

chest x-ray artificial intelligence dataset

Published: June 17, 2022. Version: 1.1.0

Database Credentialed Access

eICU Collaborative Research Database

Tom Pollard, Alistair Johnson, Jesse Raffa, Leo Anthony Celi, Omar Badawi, Roger Mark

Multi-center database comprising deidentified health data associated with over 200,000 admissions to ICUs across the United States between 2014-2015.

telemedicine icu critical care

Published: April 15, 2019. Version: 2.0

Latest Resources

Database Credentialed Access

INSPIRE, a publicly available research dataset for perioperative medicine

Hyung-Chul Lee, Leerang Lim

A public dataset that contains information related to surgery, anesthesia, laboratory results, medications, diagnosis, and outcomes from 50% of the patients who received surgery at Seoul National University Hospital between 2011 and 2020.

surgery perioperative medicine multi-center open dataset

Published: Sept. 19, 2023. Version: 0.1

Database Open Access

BIG IDEAs Lab Glycemic Variability and Wearable Device Data

Peter Cho, Juseong Kim, Brinnae Bent, Jessilyn Dunn

Glucose measurements and wrist-worn wearable sensor data from highnormoglycemic participants.

biomedical engineering pre-diabetes biomarkers

Published: Sept. 18, 2023. Version: 1.1.2

Database Credentialed Access

Medical-Diff-VQA: A Large-Scale Medical Dataset for Difference Visual Question Answering on Chest X-Ray Images

Xinyue Hu, Lin Gu, Qiyuan An, Mengliang Zhang, liangchen liu, Kazuma Kobayashi, Tatsuya Harada, Ronald Summers, Yingying Zhu

MIMIC-Diff-VQA provides a large-scale dataset for Difference visual question answering in medical chest x-ray images.

chest x-ray visual question answering difference vqa vqa difference visual question answering

Published: Sept. 15, 2023. Version: 1.0.0

Database Open Access

MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset

Brian Gow, Tom Pollard, Larry A Nathanson, Alistair Johnson, Benjamin Moody, Chrystinne Fernandes, Nathaniel Greenbaum, Jonathan W Waks, Parastou Eslami, Tanner Carbonati, Ashish Chaudhari, Elizabeth Herbst, Dana Moukheiber, Seth Berkowitz, Roger Mark, Steven Horng

The MIMIC-IV ECG module contains approximately 800,000 diagnostic electrocardiograms across nearly 160,000 unique patients. These patients overlap with the patients from the MIMIC-IV Clinical Database.

Published: Sept. 15, 2023. Version: 1.0

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Database Open Access

CheXmask Database: a large-scale dataset of anatomical segmentation masks for chest x-ray images

Nicolas Gaggion, Candelaria Mosquera, Martina Aineseder, Lucas Mansilla, Diego Milone, Enzo Ferrante

CheXmask Database is a 676,803 uniformly annotated chest radiographs with segmentation masks. Images were segmented using HybridGNet, with automatic quality control indicated by RCA scores.

chest x-ray segmentation medical image segmentation automatic quality assesment

Published: Sept. 6, 2023. Version: 0.2

Database Restricted Access

A multimodal dental dataset facilitating machine learning research and clinic services

wenjing liu, Yunyou Huang, Suqin Tang

A new dental dataset that contains 389 patients, three commonly used dental image models, and images of various health conditions of the oral cavity.

Published: Sept. 6, 2023. Version: 1.0.0

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News

MIMIC-IV-ECG module released

News from: MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset v1.0.

Sept. 15, 2023

The MIMIC-IV-ECG module is now available. This module contains approximately 800,000 diagnostic electrocardiograms across nearly 160,000 unique patients. The vast majority of ECGs for patients who appear in the MIMIC-IV Clinical Database are included. The patients in MIMIC-IV-ECG have been matched against the MIMIC-IV Clinical Database, making it possible to link to information across the MIMIC-IV modules. When a cardiologist report is available for a given ECG, we provide information for linking to it.

FFA-IR dataset is unavailable until further notice

News from: FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark v1.0.0.

Sept. 6, 2023

The authors of the FFA-IR dataset have asked for downloads to be disabled until further notice to adhere with local policy changes. We apologize for the inconvenience and hope to make the files available again in the future.

I-CARE is now available on Google Cloud

News from: I-CARE: International Cardiac Arrest REsearch consortium Database v2.0.

June 22, 2023

I-CARE v.2.0 is now available on Google Cloud. For details on downloading the dataset or working with it directly in the cloud, see the Files section of the project description.

Announcing CXR-LT, a competition for long-tailed disease classification on chest X-rays

News from: CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays v1.0.0.

June 21, 2023

We are pleased to announce CXR-LT, a competition on Multi-Label Long-Tailed Classification on Chest X-Rays. Many real-world problems, including diagnostic medical imaging exams, are “long-tailed”: there are a few common findings followed by more relatively rare conditions. This competition will provide a challenging large-scale multi-label long-tailed learning task on chest X-rays (CXRs), encouraging community engagement with this emerging interdisciplinary topic.

CXR-LT is organized as a shared task for the workshop on Computer Vision for Automated Medical Diagnosis (CVAMD) held in association with the International Conference on Computer Vision (ICCV) 2023. Participants will be invited to submit their solutions for publication presentation at CVAMD 2023 and publication in the ICCV 2023 workshop proceedings.

The challenge uses an expanded version of MIMIC-CXR-JPG v2.0.0, a large benchmark dataset for automated thorax disease classification. Each CXR study in the dataset was labeled with 12 newly added disease findings extracted from the associated radiology reports. The resulting long-tailed (LT) dataset contains 377,110 CXRs, each labeled with at least one of 26 clinical findings (including a "No Finding" class).

Important dates

05/01/2023: Development Phase begins. Participants can begin making submissions and tracking results on the public leaderboard.
07/14/2023: Testing Phase begins. Unlabeled test data will be released to registered participants. The leaderboard will be kept private for this phase.
07/17/2023: Competition ends. Participants are invited to submit their solutions as 8-page papers to ICCV CVAMD 2023!
07/28/2023: ICCV CVAMD 2023 submission deadline. (Competition participants may receive an extension if needed.)
08/11/2023: ICCV CVAMD 2023 acceptance notification.
10/06/2023: ICCV CVAMD 2023 workshop.

This competition is supported in part by the Artificial Intelligence Journal (AIJ). For any questions, please contact cxr.lt.competition.2023@gmail.com.

I-CARE will shortly be available on Google Cloud

June 20, 2023

We are aware that downloading the I-CARE dataset from PhysioNet is currently slow and we apologize for the inconvenience. To resolve this issue, we are currently transferring the dataset to Google Cloud. Once the transfer is complete, the dataset can be analyzed directly in the cloud or downloaded using Google Cloud Utilities. Please check here for updates.

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