AI RESEARCH
PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback
arXiv CS.CV
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ArXi:2506.21834v2 Announce Type: replace Inpainting, the process of filling missing or corrupted image parts, has broad applications in medical imaging. However, generating anatomically accurate synthetic polyp images for clinical AI is a largely underexplored problem. In specialized fields like gastroenterology, inaccuracies in generated images can lead to false patterns and significant errors in downstream diagnosis. To ensure reliability, models require direct feedback from domain experts like oncologists.