Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion

[2508.01334] Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion 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:2508.01334 (cs) [Submitted on 2 Aug 2025 ( v1 ), last revised 5 Aug 2025 (this version, v2)] Title: Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion Authors: Konstantinos Moutselos , Ilias Maglogiannis View a PDF of the paper titled Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion, by Konstantinos Moutselos and 1 other authors View PDF Abstract: This study proposes a zero-shot image segmentation framework for detecting erythema (redness of the skin) using edit-friendly inversion in diffusion models. The method synthesizes reference images of the same patient that are free from erythema via generative editing and then accurately aligns these references with the original images. Color-space analysis is performed with minimal user intervention to identify erythematous regions. This approach significantly reduces the reliance on labeled dermatological datasets while providing a scalable and flexible diagnostic support tool by avoiding the need for any annotated training masks. In our initial qualitative experiments, the pipeline successfully isolated facial erythema in diverse cases, demonstrating performance improvements over baseline threshold-based techniques. These results highlight the potential of combining generative diffusion models and statistical color segmentation for computer-aided dermatology, enabling efficient erythema detection without prior training data. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2508.01334 [cs.CV] (or arXiv:2508.01334v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2508.01334 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Proc. International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD), 2025, pp. 115_125 Related DOI : https://doi.org/10.1007/978-981-95-7425-4_11 Focus to learn more DOI(s) linking to related resources Submission history From: Konstantinos Moutselos [ view email ] [v1] Sat, 2 Aug 2025 12:00:35 UTC (462 KB) [v2] Tue, 5 Aug 2025 07:33:34 UTC (463 KB) Full-text links: Access Paper: View a PDF of the paper titled Zero-shot Segmentation of Skin Conditions: Erythema with Edit-Friendly Inversion, by Konstantinos Moutselos and 1 other authors View PDF view license Current browse context: cs.CV < prev | next > new | recent | 2025-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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