Resources


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, et al.

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

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

Published: Feb. 3, 2025. Version: 1.0.1

Database Credentialed Access

MIMIC-IV-ECHO-Ext-MIMICEchoQA: A Benchmark Dataset for Echocardiogram-Based Visual Question Answering

Rahul Thapa, Andrew Li, Qingyang Wu, et al.

We present MIMICEchoQA, a benchmark dataset for echocardiogram-based question answering, built from the publicly available MIMIC-IV-ECHO database.

Published: Oct. 7, 2025. Version: 1.0.0

Database Credentialed Access

MIMIC-Ext-CXR-QBA: A Structured, Tagged, and Localized Visual Question Answering Dataset with Question-Box-Answer Triplets and Scene Graphs for Chest X-ray Images

Philip Müller, Friederike Jungmann, Georgios Kaissis, et al.

We present a large-scale CXR VQA dataset derived from MIMIC-CXR with 42M QA pairs, featuring hierarchical answers, bounding boxes, and structured tags. We generated QA-pairs using LLM-based extraction from radiology reports and localization models.

chest x-rays vqa localization scene graphs

Published: July 22, 2025. Version: 1.0.0

Database Restricted Access

Visual Question Answering evaluation dataset for MIMIC CXR

Timo Kohlberger, Charles Lau, Tom Pollard, et al.

This dataset provides 224 VQAs for 40 test set cases, and 111 VQAs for 23 validation set cases of the MIMIC CXR dataset.

Published: Jan. 28, 2025. Version: 1.0.0

Database Credentialed Access

Medical-CXR-VQA dataset: A Large-Scale LLM-Enhanced Medical Dataset for Visual Question Answering on Chest X-Ray Images

Xinyue Hu, Lin Gu, Kazuma Kobayashi, et al.

Medical-CXR-VQA provides a large-scale LLM-enhanced dataset for visual question answering in medical chest x-ray images.

Published: Jan. 21, 2025. 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 deep learning benchmark multimodal visual question answering

Published: July 19, 2024. Version: 1.0.0

Database Credentialed Access

CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays

Hyungyung Lee, Geon Choi, Jung Oh Lee, et al.

CheXStruct is an automated pipeline that derives structured diagnostic reasoning steps from chest X-rays. CXReasonBench builds on this to evaluate whether models perform clinically grounded, multi-step reasoning beyond final diagnoses.

evaluation chest x-ray benchmark structured chest x-ray qa intermediate reasoning steps structured reasoning grounded reasoning diagnostic reasoning structured diagnostic pipeline

Published: Oct. 23, 2025. Version: 1.0.1

Database Open Access

Hillel Yaffe Glaucoma Dataset (HYGD): A Gold-Standard Annotated Fundus Dataset for Glaucoma Detection

Or Abramovich, Hadas Pizem, Jonathan Fhima, et al.

HYGD is a rigorously annotated fundus image dataset with gold-standard clinical labels designed to improve and benchmark deep learning models for accurate glaucoma detection.

ophthalmology retina glaucoma dfi gon fundus gold-standard

Published: March 16, 2026. Version: 1.1.0

Database Credentialed Access

MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

Zishan Gu, Jiayuan Chen, Fenglin Liu, et al.

MedVH provides a visual hallucination evaluation benchmark for large language models in the medical context. It formulates tests using chest X-ray images, including multi-choice question answering and long-text generation tasks.

Published: Dec. 10, 2025. Version: 1.0.1

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