Resources


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, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei JI, Eric Chang, Tackeun Kim, Edward Choi

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

Published: July 23, 2024. Version: 1.0.0

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, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei JI, Eric Chang, Tackeun Kim, Edward Choi

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

Published: July 23, 2024. Version: 1.0.0

Database Open Access

A Multi-Modal Satellite Imagery Dataset for Public Health Analysis in Colombia

Sebastian A Cajas, David Restrepo, Dana Moukheiber, Kuan Ting Kuo, Chenwei Wu, David Santiago Garcia Chicangana, Atika Rahman Paddo, Mira Moukheiber, Lama Moukheiber, Sulaiman Moukheiber, Saptarshi Purkayastha, Diego M Lopez, Po-Chih Kuo, Leo Anthony Celi

Multi-Modal Satellite imagery Dataset in Colombia: A public health analysis with spatiotemporally aligned satellite images and its corresponding metadata across 81 municipalities (2016-2018), facilitating multimodal AI applications.

multimodality satellite imagery

Published: Jan. 30, 2024. Version: 1.0.0

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

Database Credentialed Access

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Matthew Lungren, Andrew Ng, Curtis Langlotz, Pranav Rajpurkar

RadGraph is a dataset of entities and relations in full-text chest X-ray radiology reports, which are obtained using a novel information extraction (IE) schema to capture clinically relevant information in a radiology report.

entity and relation extraction graph multi-modal natural language processing radiology

Published: June 3, 2021. 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, Eunbyeol Cho, Gyubok Lee, Sunjun Kweon, Jungwoo Oh, Lei JI, Eric Chang, Tackeun Kim, Edward Choi

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

Published: July 19, 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, Fernando Pérez-García, Max Ilse, Daniel Coelho de Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anton Schwaighofer, Maria Teodora Wetscherek, Hannah Richardson, Tristan Naumann, Javier Alvarez Valle, Ozan Oktay

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.

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

Published: March 17, 2023. Version: 1.0.0

Database Credentialed Access

Eye Gaze Data for Chest X-rays

Alexandros Karargyris, Satyananda Kashyap, Ismini Lourentzou, Joy Wu, Matthew Tong, Arjun Sharma, Shafiq Abedin, David Beymer, Vandana Mukherjee, Elizabeth Krupinski, Mehdi Moradi

This dataset was a collected using an eye tracking system while a radiologist interpreted and read 1,083 public CXR images. The dataset contains the following aligned modalities: image, transcribed report text, dictation audio and eye gaze data.

audio convolutional network heatmap eye tracking explainability chest cxr multimodal radiology machine learning deep learning chest x-ray

Published: Sept. 12, 2020. Version: 1.0.0

Software Credentialed Access

Code for generating the HAIM multimodal dataset of MIMIC-IV clinical data and x-rays

Luis R Soenksen, Yu Ma, Cynthia Zeng, Leonard David Jean Boussioux, Kimberly Villalobos Carballo, Liangyuan Na, Holly Wiberg, Michael Li, Ignacio Fuentes, Dimitris Bertsimas

Code for generating the HAIM multimodal dataset of MIMIC-IV clinical data and x-rays

database code multimodality

Published: Aug. 23, 2022. Version: 1.0.1

Database Credentialed Access

DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

Jayetri Bardhan, Anthony Colas, Kirk Roberts, Daisy Zhe Wang

DrugEHRQA is a QA dataset containing question-answers from MIMIC-III tables and discharge summaries.

question-answer qa

Published: April 12, 2022. Version: 1.0.0