Medico 2025: Visual Question Answering for Gastrointestinal Imaging

Sushant Gautam, Vajira Thambawita, Michael Riegler, Pål Halvorsen, Steven Hicks
8/14/2025
cs.CVcs.AI

Official code: simula/MediaEval-Medico-2025 (7 stars, last commit 2026-02-24).

Abstract

The Medico 2025 challenge addresses Visual Question Answering (VQA) for Gastrointestinal (GI) imaging, organized as part of the MediaEval task series. The challenge focuses on developing Explainable Artificial Intelligence (XAI) models that answer clinically relevant questions based on GI endoscopy images while providing interpretable justifications aligned with medical reasoning. It introduces two subtasks: (1) answering diverse types of visual questions using the Kvasir-VQA-x1 dataset, and (2) generating multimodal explanations to support clinical decision-making. The Kvasir-VQA-x1 dataset, created from 6,500 images and 159,549 complex question-answer (QA) pairs, serves as the benchmark for the challenge. By combining quantitative performance metrics and expert-reviewed explainability assessments, this task aims to advance trustworthy Artificial Intelligence (AI) in medical image analysis. Instructions, data access, and an updated guide for participation are available in the official competition repository: https://github.com/simula/MediaEval-Medico-2025

View on arXivView PDF

Code Implementations(1)

simula/MediaEval-Medico-2025Official100%

Official repository for the MediaEval Medico 2025: VQA (with multimodal explanations) for GastroIntestinal Imaging, featuring the Kvasir-VQA-x1 dataset, participation guidelines, and starter resources.

71Jupyter NotebookMay 8, 20257 months ago
datasetexplainable-aigastroenterologykvasirmediaeval+4 more

Similar Papers

Multimodal AI for Gastrointestinal Diagnostics: Tackling VQA in MEDVQA-GI 2025

88% match2025

PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering

80% match1 repo2382023

MedXplain-VQA: Multi-Component Explainable Medical Visual Question Answering

80% match2025

Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoning

79% match1 repo262025

Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis

79% match2026

Multi-Level CLS Token Fusion for Contrastive Learning in Endoscopy Image Classification

79% match2025

Cite this paper

@article{gautam2025medico,
  title  = {Medico 2025: Visual Question Answering for Gastrointestinal Imaging},
  author = {Sushant Gautam and Vajira Thambawita and Michael Riegler and Pål Halvorsen and Steven Hicks},
  year   = {2025},
  eprint = {2508.10869},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.10869v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2508.10869.svg)](https://codeofpaper.com/paper/2508.10869)

Discussion