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Database Restricted Access

VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs

Hieu Huy Pham, Hieu Nguyen Trung, Ha Quy Nguyen

VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs

Published: Aug. 24, 2021. Version: 1.0.0

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 657,566 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: March 1, 2024. Version: 0.4

Database Credentialed Access

CXR-PRO: MIMIC-CXR with Prior References Omitted

Vignav Ramesh, Nathan Chi, Pranav Rajpurkar

CXR-PRO is an adaptation of the MIMIC-CXR dataset (consisting of chest radiographs and their associated free-text radiology reports) with references to non-existent priors removed.

generation free-text radiology reports references to priors retrieval large language models

Published: Nov. 23, 2022. Version: 1.0.0

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 657,566 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: March 1, 2024. Version: 0.4

Database Restricted Access

Endoscapes2023, A Critical View of Safety and Surgical Scene Segmentation Dataset for Laparoscopic Cholecystectomy

Pietro Mascagni, Deepak Alapatt, Aditya Murali, Armine Vardazaryan, Alain Garcia Vazquez, Nariaki Okamoto, Guido Costamagna, Didier Mutter, Jacques Marescaux, Bernard Dallemagne, Nicolas Padoy

Endoscapes2023 enables the development of models for object detection, semantic and instance segmentation, and Critical View of Safety (CVS) prediction, contributing to safe laparoscopic cholecystectomy.

surgical safety computer assisted interventions semantic segmentation surgical data science medical imaging analysis

Published: Dec. 11, 2024. Version: 1.0.0

Database Open Access

Eye Tracking Dataset for the 12-Lead Electrocardiogram Interpretation of Medical Practitioners and Students

Mohammed Tahri Sqalli, Dena Al-Thani, Mohamed Elshazly, Mohammed Al-Hijji

The project aims at collecting a dataset using eye-tracking technology to understand the 12-lead electrocardiogram interpretation visual behavior for medical practitioners and students with different expertise levels.

human vision medical students ecg interpretation medical image interpretation medical practice medical education human-computer interaction eye-tracking medical practitioners visual expertise ecg electrocardiogram

Published: March 16, 2022. Version: 1.0.0

Database Restricted Access

Endoscapes2023, A Critical View of Safety and Surgical Scene Segmentation Dataset for Laparoscopic Cholecystectomy

Pietro Mascagni, Deepak Alapatt, Aditya Murali, Armine Vardazaryan, Alain Garcia Vazquez, Nariaki Okamoto, Guido Costamagna, Didier Mutter, Jacques Marescaux, Bernard Dallemagne, Nicolas Padoy

Endoscapes2023 enables the development of models for object detection, semantic and instance segmentation, and Critical View of Safety (CVS) prediction, contributing to safe laparoscopic cholecystectomy.

surgical safety computer assisted interventions semantic segmentation surgical data science medical imaging analysis

Published: Dec. 11, 2024. Version: 1.0.0

Database Open Access

Eye Tracking Dataset for the 12-Lead Electrocardiogram Interpretation of Medical Practitioners and Students

Mohammed Tahri Sqalli, Dena Al-Thani, Mohamed Elshazly, Mohammed Al-Hijji

The project aims at collecting a dataset using eye-tracking technology to understand the 12-lead electrocardiogram interpretation visual behavior for medical practitioners and students with different expertise levels.

human vision medical students ecg interpretation medical image interpretation medical practice medical education human-computer interaction eye-tracking medical practitioners visual expertise ecg electrocardiogram

Published: March 16, 2022. Version: 1.0.0