This paper describes our approach to Subtask 1 of the ImageCLEFmed MEDVQA 2025 Challenge, which targets visual question answering (VQA) for gastrointestinal endoscopy. We adopt the Florence model-a large-scale multimodal foundation model-as the backbone of our VQA pipeline, pairing a powerful vision encoder with a text encoder to interpret endoscopic images and produce clinically relevant answers. To improve generalization, we apply domain-specific augmentations that preserve medical features while increasing training diversity. Experiments on the KASVIR dataset show that fine-tuning Florence yields accurate responses on the official challenge metrics. Our results highlight the potential of large multimodal models in medical VQA and provide a strong baseline for future work on explainability, robustness, and clinical integration. The code is publicly available at: https://github.com/TiwariLaxuu/VQA-Florence.git
@article{arxiv.2507.14544,
title = {Multimodal AI for Gastrointestinal Diagnostics: Tackling VQA in MEDVQA-GI 2025},
author = {Sujata Gaihre and Amir Thapa Magar and Prasuna Pokharel and Laxmi Tiwari},
journal= {arXiv preprint arXiv:2507.14544},
year = {2025}
}
Comments
accepted to ImageCLEF 2025, to be published in the lab proceedings