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Querying GI Endoscopy Images: A VQA Approach

arXiv 2507.21165 ↗First posted 25 Jul 2025eess.IV cs.CVcited by 0

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Abstract (arXiv)

VQA (Visual Question Answering) combines Natural Language Processing (NLP) with image understanding to answer questions about a given image. It has enormous potential for the development of medical diagnostic AI systems. Such a system can help clinicians diagnose gastro-intestinal (GI) diseases accurately and efficiently. Although many of the multimodal LLMs available today have excellent VQA capabilities in the general domain, they perform very poorly for VQA tasks in specialized domains such as medical imaging. This study is a submission for ImageCLEFmed-MEDVQA-GI 2025 subtask 1 that explores the adaptation of the Florence2 model to answer medical visual questions on GI endoscopy images. We also evaluate the model performance using standard metrics like ROUGE, BLEU and METEOR

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 25 Jul 2025.

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References · 5

  1. 2023Florence-2: Advancing a Unified Representation for a Variety of Vision Taskscited by 403
  2. 2020Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopycited by 54
  3. 2024Kvasir-VQA: A Text-Image Pair GI Tract Datasetcited by 23
  4. 2025Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopycited by 11
  5. 2025Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Modelscited by 5

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  1. 2025Multimodal AI for Gastrointestinal Diagnostics: Tackling VQA in MEDVQA-GI 2025
  2. 2026Towards Grounded GI Endoscopy VQA via Multi-Task Learning on Small VLMs
  3. 2026Parameter-Efficient VLMs for Gastrointestinal Endoscopy: Medical Image Generation and Clinical Visual Question Answering
  4. 2026SurgCheck: Do Vision-Language Models Really Look at Images in Surgical VQA?
  5. 2025Are Vision Language Models Ready for Clinical Diagnosis? A 3D Medical Benchmark for Tumor-centric Visual Question Answering
  6. 2023Interpretable Medical Image Visual Question Answering via Multi-Modal Relationship Graph Learning
  7. 2025A Lightweight Large Vision-language Model for Multimodal Medical Images
  8. 2025SurgViVQA: Temporally-Grounded Video Question Answering for Surgical Scene Understanding