Resources


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 Credentialed Access

MIMIC-III-Ext-tPatchGNN

Chenlong Yin, Weijia Zhang

The processed MIMIC-III dataset for the benchmark of Irregular Multivariate Time Series Forecasting: A Transformable Patching Graph Neural Networks Approach.

Published: April 9, 2025. Version: 1.0.0

Model Credentialed Access

Medical AI Research Foundations: A repository of medical foundation models

Shekoofeh Azizi, Jan Freyberg, Laura Culp, et al.

Medical AI Research Foundations is a repository of medical foundation models.

Published: April 25, 2023. Version: 1.0.0

Database Credentialed Access

MIMIC-Ext-MIMIC-CXR-VQA: A Complex, Diverse, And Large-Scale Visual Question Answering Dataset for Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, et al.

We introduce MIMIC-Ext-MIMIC-CXR-VQA, a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs.

question answering machine learning electronic health records evaluation chest x-ray radiology benchmark multimodal deep learning visual question answering

Published: July 19, 2024. Version: 1.0.0

Database Credentialed Access

MIMIC-Ext-MIMIC-CXR-VQA: A Complex, Diverse, And Large-Scale Visual Question Answering Dataset for Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, et al.

We introduce MIMIC-Ext-MIMIC-CXR-VQA, a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs.

question answering machine learning electronic health records evaluation chest x-ray radiology benchmark multimodal deep learning visual question answering

Published: July 19, 2024. Version: 1.0.0

Database Credentialed Access

RuMedNLI: A Russian Natural Language Inference Dataset For The Clinical Domain

Pavel Blinov, Aleksandr Nesterov, Galina Zubkova, et al.

RuMedNLI is the full counterpart dataset of MedNLI in Russian language.

natural language inference recognizing textual entailment russian language

Published: April 1, 2022. Version: 1.0.0

Database Open Access

NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research

Danilo Pani, Eleonora Sulas, Monica Urru, et al.

Open dataset featuring non-invasive electrophysiological recordings, fetal pulsed-wave Doppler and maternal respiration signals. It provides a ground truth on the fetal heart activity when an invasive scalp lead is unavailable.

foetus pwd doppler foetal ecg maternal ecg pwd envelope non-invasive cardiology early pregnancy antenatal fecg ecg

Published: Nov. 12, 2020. Version: 1.0.0

Visualize waveforms

Model Credentialed Access

Me-LLaMA: Foundation Large Language Models for Medical Applications

Qianqian Xie, Qingyu Chen, Aokun Chen, et al.

Me-LLaMA is a family of large language models for medical applications trained using clinical text with LLaMA2 models as the base. We release model weights for the foundation models as well as the chat-enhanced models.

large language models

Published: June 5, 2024. Version: 1.0.0

Database Restricted Access

Swiss-Mammo: A physician-written, synthetic dataset of German mammography reports

Daniel Reichenpfader, Sandro von Däniken, Harald Marcel Bonel

Swiss-Mammo: A physician-written, synthetic dataset of 28 German mammography reports. The dataset is stratified based on BI-RADS categories and available in German and English.

radiology mammography structured reporting bi-rads

Published: June 24, 2025. Version: 1.0.1

Database Credentialed Access

EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, et al.

We present EHRXQA, the first multi-modal EHR QA dataset combining structured patient records with aligned chest X-ray images. EHRXQA contains a comprehensive set of QA pairs covering image-related, table-related, and image+table-related questions.

question answering machine learning electronic health records evaluation chest x-ray multi-modal question answering ehr question answering semantic parsing benchmark deep learning visual question answering

Published: July 23, 2024. Version: 1.0.0