Enhancing Skin Lesion Segmentation via Martingale Feature Fusion and Adaptive Deep Semantic Modeling

Enhancing Skin Lesion Segmentation via Martingale Feature Fusion and Adaptive Deep Semantic Modeling | 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 Enhancing Skin Lesion Segmentation via Martingale Feature Fusion and Adaptive Deep Semantic Modeling Yao Lu, Yan Zhao, Shigang Wang, Jian Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8630199/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Skin lesion segmentation from medical images is crucial for early disease detection, yet it faces challenges due to complex textures and blurred boundaries. This paper introduces the Martingale Feature Fusion Network (MFFNet), a novel segmentation framework that leverages martingale-basedstatistical texture modeling to enhance feature expressiveness. MFFNet integrates a texture martingale module for robust texture representation, a cross-attention fusion module for multi-modal feature interaction, and a deep semantic fusion module for dynamic feature response adjustment.Experiments on ISIC2016, ISIC2017, and ISIC2018 datasets demonstrate that MFFNet outperforms existing hybrid architectures, particularly in challenging scenarios, achieving state-of-the-art performance with mean Intersection over Union (mIoU) scores of 0.8601, 0.7853, and 0.8177, respectively.These results validate the effectiveness of martingale-based texture modeling in improving segmentation accuracy. The source code is available at https://github.com/lyao519/MFFseg . Martingale Medical Image Segmentation Convolutional Neural Network Transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviews received at journal 01 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers agreed at journal 21 Feb, 2026 Reviewers invited by journal 13 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 18 Jan, 2026 First submitted to journal 18 Jan, 2026 You are reading this latest preprint version 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. 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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-8630199","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590944144,"identity":"e94157ee-1f6c-4dea-9621-6786b0189fd4","order_by":0,"name":"Yao Lu","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Lu","suffix":""},{"id":590944145,"identity":"ee3f29e9-ebbd-4476-98b3-c0ceeef76cb9","order_by":1,"name":"Yan Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACCTBpw8wHonhI0JLGzEaqlsMMxGuRn9388AFj23l2NokExgdv2xjkzQlpYZxzzNiAse02M1ALs+HcNgbDnQ0EtDBLJJhJQLWwSfO2MSQYHCCghU0i/RtQyzmQFvbfRGnhkcgB2XIAbAszUVokJHKKDRjOJTOz8TxslpxzTsJwAyEt8jPSNz5gKLNL5mdPPvjhTZmNPEFbQID5LxtDMjDwGhhg0UQE+MNgR6zSUTAKRsEoGIEAAEt5Mm0k5vX6AAAAAElFTkSuQmCC","orcid":"","institution":"Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhao","suffix":""},{"id":590944146,"identity":"d9aa6b6c-c8f9-4673-9c7b-92f5a4f607b3","order_by":2,"name":"Shigang Wang","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Shigang","middleName":"","lastName":"Wang","suffix":""},{"id":590944147,"identity":"ec23c4da-fa1d-4e65-ae51-c1f61c4d141d","order_by":3,"name":"Jian Wei","email":"","orcid":"","institution":"Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2026-01-18 09:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8630199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8630199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103049560,"identity":"3b2d0ae7-9382-4c38-a7e0-0237b076fbfd","added_by":"auto","created_at":"2026-02-20 07:42:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1175741,"visible":true,"origin":"","legend":"","description":"","filename":"MFFNetVC1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8630199/v1_covered_b53c8c0f-6088-461c-83c0-04c42e01110d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Skin Lesion Segmentation via Martingale Feature Fusion and Adaptive Deep Semantic Modeling","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Martingale, Medical Image Segmentation, Convolutional Neural Network, Transformer","lastPublishedDoi":"10.21203/rs.3.rs-8630199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8630199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSkin lesion segmentation from medical images is crucial for early disease detection, yet it faces challenges due to complex textures and blurred boundaries. This paper introduces the Martingale Feature Fusion Network (MFFNet), a novel segmentation framework that leverages martingale-basedstatistical texture modeling to enhance feature expressiveness. MFFNet integrates a texture martingale module for robust texture representation, a cross-attention fusion module for multi-modal feature interaction, and a deep semantic fusion module for dynamic feature response adjustment.Experiments on ISIC2016, ISIC2017, and ISIC2018 datasets demonstrate that MFFNet outperforms existing hybrid architectures, particularly in challenging scenarios, achieving state-of-the-art performance with mean Intersection over Union (mIoU) scores of 0.8601, 0.7853, and 0.8177, respectively.These results validate the effectiveness of martingale-based texture modeling in improving segmentation accuracy. 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