WITHDRAWN: Towards Robust Eczema Region Identification in Clinical Skin Images via Domain-Adaptive Deep Hashing

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Abstract

Abstract Accurate identification of eczema-affected regions is a critical step toward reliable computer-aided dermatological analysis, yet the scarcity of labeled data in clinical settings limits model performance. To address this challenge, we explore domain adaptation as a strategy to leverage richly annotated data from related skin conditions while adapting to target domains with sparse labels. We introduce a novel deep learning framework that integrates labeled source data with unlabeled target samples, enabling the model to learn transferable representations that remain robust across domain shifts. A curated dataset of dermatoscopic images, including eczema regions and other dermatological conditions, is constructed to evaluate the effectiveness of the proposed approach. By combining domain-adaptive feature extraction with deep hashing mechanisms, the framework enhances cross-domain generalization and accurately localizes eczema regions in previously unseen images. Experimental results demonstrate substantial improvements over conventional baselines in both recognition accuracy and robustness, highlighting the potential of the method to advance automated eczema detection for real-world clinical practice.
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WITHDRAWN: Towards Robust Eczema Region Identification in Clinical Skin Images via Domain-Adaptive Deep Hashing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article WITHDRAWN: Towards Robust Eczema Region Identification in Clinical Skin Images via Domain-Adaptive Deep Hashing Sophia Hoshino, Min Seo, Soo Jung Kim, Michael Yamamoto, Seojin Lim, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7473010/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Editorial Note 4 September, 2025. Research Square has withdrawn this preprint due to significant concerns regarding the integrity of the work. Maintaining high standards of ethical conduct is essential to our platform. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract 4 September, 2025. Research Square has withdrawn this preprint due to significant concerns regarding the integrity of the work. Maintaining high standards of ethical conduct is essential to our platform. Nuclear Medicine & Medical Imaging Full Text 4 September, 2025. Research Square has withdrawn this preprint due to significant concerns regarding the integrity of the work. Maintaining high standards of ethical conduct is essential to our platform. Additional Declarations Ethics Statement: This study was approved by the Institutional Review Board (IRB) of our research institute (CureAI Research). All participants provided informed consent before data collection, and the study was conducted in accordance with ethical guidelines. The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7473010","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510441024,"identity":"86b69b38-7e80-480f-83fb-c80353493562","order_by":0,"name":"Sophia Hoshino","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sophia","middleName":"","lastName":"Hoshino","suffix":""},{"id":510441025,"identity":"0beac588-4e3e-4c83-9c1b-2e8d5068b737","order_by":1,"name":"Min Seo","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Seo","suffix":""},{"id":510441026,"identity":"a8d4d46d-3bab-4994-a76c-cc389547a3a9","order_by":2,"name":"Soo Jung Kim","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Soo","middleName":"Jung","lastName":"Kim","suffix":""},{"id":510441027,"identity":"3d1d33f4-c123-46b6-bf89-2895c2b1b414","order_by":3,"name":"Michael Yamamoto","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Yamamoto","suffix":""},{"id":510441028,"identity":"a8a0090e-3dcf-4e2a-bda3-5ee5fd871e37","order_by":4,"name":"Seojin Lim","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Seojin","middleName":"","lastName":"Lim","suffix":""},{"id":510441029,"identity":"a6e59a97-e74c-40fe-83b5-cbe4307a05aa","order_by":5,"name":"Gyu-Jin An","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYJACA4kfNnL8INYDIIc4LZY9acaSDQyMDQnEamGoYDucaHCAWC38/csvFNzgYU4wPt5j/iChwsaYgf3w0Q34tEjceFNgOMOCLc/szBnDhoQzaWYMPGlpN/Bac+NMgrEED0+x2Y0cw4bEtsM2DBI8Zni1yIO0/GGTSNw8g1gtBufbDxhIsBkkbpCAaDEjqMXwBg+DgWQP0HFnjhXOAPrFmI2QX+TOH38GjMr/cvztzRs+fKiwMexnP3wMv/clcsxQo4INr3IQ4D/++AFBRaNgFIyCUTCyAQDw+092vl9lIwAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Gyu-Jin","middleName":"","lastName":"An","suffix":""},{"id":510441030,"identity":"579cb5c3-cbac-44f2-8e9f-0c885a809b84","order_by":6,"name":"James Morita","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"Morita","suffix":""}],"badges":[],"createdAt":"2025-08-27 15:13:08","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7473010/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-7473010/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"\u003cp\u003e4 September, 2025. 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