Resources


Database Credentialed Access

RadGraph-XL: A Large-Scale Expert-Annotated Dataset for Entity and Relation Extraction from Radiology Reports

Jean-Benoit Delbrouck

RadGraph-XL is a large, expert-annotated dataset of 2,300 radiology reports covering multiple modalities and anatomies. It enables accurate extraction of clinical entities and relations for downstream medical AI tasks.

Published: Sept. 12, 2025. Version: 1.0.0

Database Contributor Review

ER-REASON: A Benchmark Dataset for LLM-Based Clinical Reasoning in the Emergency Room

Mel Molina, Nikita Mehandru, Niloufar Golchini, et al.

The ER-REASON dataset is a longitudinal collection of 25,174 de-identified clinical notes for 3,437 patients admitted to the emergency room (ER) at a large academic medical center between March 1, 2022, and March 31, 2024.

Published: Oct. 23, 2025. Version: 1.0.0

Model Credentialed Access

RadVLM model

Nicolas Deperrois, Hidetoshi Matsuo, Samuel Ruiperez-Campillo, et al.

RadVLM is a 7B-parameter vision-language model fine-tuned on public chest-X-ray data that drafts reports, lists abnormalities, grounds findings, and chats about a CXR through a single image-to-text interface.

Published: Oct. 8, 2025. Version: 1.0.0

Database Open Access

Wearable Device Dataset from Induced Stress and Structured Exercise Sessions

Andrea Hongn, Facundo Bosch, Lara Prado, et al.

Physiological signals(Electrodermal Activity,Blood Volume Pulse, Heart Rate, Temperature,etc) from 36 healthy volunteers collected during structured acute stress induction and aerobic/anaerobic exercise sessions using the Empatica E4 wearable device.

exercise stress wearable aerobic anaerobic

Published: June 24, 2025. Version: 1.0.1

Database Credentialed Access

CXR-Align: A Benchmark for CXR-Report Alignment with Negations

Hanbin Ko

CXR-Align is a benchmark dataset created to evaluate vision-language models' capability to interpret negations in chest X-ray (CXR) reports, featuring systematically modified reports from MIMIC-CXR.

Published: Aug. 21, 2025. Version: 1.0.0

Database Restricted Access

Pulmonary Edema Severity Grades Based on MIMIC-CXR

Ruizhi Liao, Geeticka Chauhan, Polina Golland, et al.

Pulmonary edema metadata and labels for MIMIC-CXR

Published: Feb. 9, 2021. Version: 1.0.1

Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, et al.

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.

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

Published: July 13, 2021. Version: 1.0.0

Database Restricted Access

CheXchoNet: A Chest Radiograph Dataset with Gold Standard Echocardiography Labels

Pierre Elias, Shreyas Bhave

Early detection of heart failure is vital for improving outcomes. The dataset contains 71,589 CXRs paired with gold standard labels from echocardiograms to enable the training of models to detect pathologies indicative of early stage heart failure.

chest x-rays heart failure early detection cardiac structural abnormalties deep learning

Published: March 20, 2024. Version: 1.0.0

Database Credentialed Access

MS-CXR-T: Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Shruthi Bannur, Stephanie Hyland, Qianchu Liu, et al.

The MS-CXR-T is a multimodal benchmark that enhances the MIMIC-CXR v2 dataset by including expert-verified annotations. Its goal is to evaluate biomedical visual-language processing models in terms of temporal semantics extracted from image and text.

disease progression cxr vision-language processing chest x-ray radiology multimodal

Published: March 17, 2023. Version: 1.0.0

Database Restricted Access

CheXchoNet: A Chest Radiograph Dataset with Gold Standard Echocardiography Labels

Pierre Elias, Shreyas Bhave

Early detection of heart failure is vital for improving outcomes. The dataset contains 71,589 CXRs paired with gold standard labels from echocardiograms to enable the training of models to detect pathologies indicative of early stage heart failure.

chest x-rays heart failure early detection cardiac structural abnormalties deep learning

Published: March 20, 2024. Version: 1.0.0