Multimodal AI for Gastrointestinal Diagnostics: Tackling VQA in MEDVQA-GI 2025
This work addresses medical diagnostics for clinicians using an incremental approach based on existing models.
The paper tackled visual question answering for gastrointestinal endoscopy by fine-tuning the Florence multimodal model, achieving accurate results on the KASVIR dataset as part of the MEDVQA-GI 2025 challenge.
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