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Medical Visual Question Answering datasets
archive 2025-07-28
11 datasets carry the task tag "Medical Visual Question Answering" (the task itself: Medical Visual Question Answering), ordered by the archive's paper count. Page 1 of 1: 11 shown of 11. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
Medical Visual Question Answering datasets 1–11 of 11
VQA-RAD (Visual Question Answering in Radiology)
VQA-RAD consists of 3,515 question–answer pairs on 315 radiology images.
145 papers · 0 benchmarks
PathVQA consists of 32,799 open-ended questions from 4,998 pathology images where each question is manually checked to ensure correctness.
89 papers · 0 benchmarks
SLAKE is an English-Chinese bilingual dataset consisting of 642 images and 14,028 question-answer pairs for training and testing Med-VQA systems.
63 papers · 0 benchmarks
PMC-VQA is a large-scale medical visual question-answering dataset that contains 227k VQA pairs of 149k images that cover various modalities or diseases.
55 papers · 2 benchmarks
Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online.
17 papers · 0 benchmarks
English subset of the SLAKE dataset, comprising 642 images and more than 7,000 question–answer pairs.
7 papers · 0 benchmarks
OVQA contains 19,020 medical visual question and answer pairs generated from 2,001 medical images collected from 2,212 EMRs in Orthopedics.
4 papers · 0 benchmarks
The Kvasir-VQA dataset is an extended dataset derived from the HyperKvasir and Kvasir-Instrument datasets, augmented with question-and-answer annotations.
3 papers · 0 benchmarks
A new in-context visual question answering dataset encompassing interleaved image and EHR data derived from MIMIC-IV and MIMIC-CXR-JPG databases.
1 paper · 0 benchmarks
Click to add a brief description of the dataset (Markdown and LaTeX enabled).
1 paper · 0 benchmarks
MediConfusion is a challenging medical Visual Question Answering (VQA) benchmark dataset, that probes the failure modes of medical Multimodal Large Language Models (MLLMs) from a vision perspective.
1 paper · 0 benchmarks
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.