Medico 2025: Visual Question Answering for Gastrointestinal Imaging
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| Format: | Preprint |
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2025
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| _version_ | 1866915446832234496 |
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| author | Gautam, Sushant Thambawita, Vajira Riegler, Michael Halvorsen, Pål Hicks, Steven |
| author_facet | Gautam, Sushant Thambawita, Vajira Riegler, Michael Halvorsen, Pål Hicks, Steven |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_10869 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Medico 2025: Visual Question Answering for Gastrointestinal Imaging Gautam, Sushant Thambawita, Vajira Riegler, Michael Halvorsen, Pål Hicks, Steven Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 92C55 I.2.10; I.4.9 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 |
| title | Medico 2025: Visual Question Answering for Gastrointestinal Imaging |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence 68T45, 92C55 I.2.10; I.4.9 |
| url | https://arxiv.org/abs/2508.10869 |