PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback

[2506.21834] PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2506.21834 (cs) [Submitted on 27 Jun 2025 ( v1 ), last revised 12 Apr 2026 (this version, v2)] Title: PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback Authors: Duy-Bao Bui , Hoang-Khang Nguyen , Thao Thi Phuong Dao , Kim Anh Phung , Tam V. Nguyen , Justin Zhan , Minh-Triet Tran , Trung-Nghia Le View a PDF of the paper titled PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback, by Duy-Bao Bui and Hoang-Khang Nguyen and Thao Thi Phuong Dao and Kim Anh Phung and Tam V. Nguyen and Justin Zhan and Minh-Triet Tran and Trung-Nghia Le View PDF HTML (experimental) Abstract: 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. We propose PrefPaint, an interactive system that incorporates expert human feedback into Stable Diffusion Inpainting. By using D3PO instead of full RLHF, our approach bypasses the need for computationally expensive reward models, making it a highly practical choice for resource-constrained clinical settings. Furthermore, we introduce a streamlined web-based interface to facilitate this expert-in-the-loop training. Central to this platform is the Model Tree versioning interface, a novel HCI concept that visualizes the evolutionary progression of fine-tuned models. This interactive interface provides a smooth and intuitive user experience, making it easier to offer feedback and manage the fine-tuning process. User studies show that PrefPaint outperforms existing methods, reducing visual inconsistencies and generating highly realistic, anatomically accurate polyp images suitable for clinical AI applications. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2506.21834 [cs.CV] (or arXiv:2506.21834v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2506.21834 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Trung Nghia Le [ view email ] [v1] Fri, 27 Jun 2025 00:47:07 UTC (41,373 KB) [v2] Sun, 12 Apr 2026 01:02:59 UTC (18,216 KB) Full-text links: Access Paper: View a PDF of the paper titled PrefPaint: Enhancing Medical Image Inpainting through Expert Human Feedback, by Duy-Bao Bui and Hoang-Khang Nguyen and Thao Thi Phuong Dao and Kim Anh Phung and Tam V. Nguyen and Justin Zhan and Minh-Triet Tran and Trung-Nghia Le View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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