Generative Model-Based Fundus Photography Translation for Enhanced Cross-Device Consistency

Generative Model-Based Fundus Photography Translation for Enhanced Cross-Device Consistency | 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 Article Generative Model-Based Fundus Photography Translation for Enhanced Cross-Device Consistency Jaehan Joo, Ji-Eun Lee, Su-Jin Kim, Seung Min Lee, Suk Chan Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7229849/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 We propose a novel image translation framework that converts fundus images from conventional fundus cameras to confocal scanning laser ophthalmoscopy (cSLO), aiming to bridge a clinically significant domain gap that has been largely overlooked. Our model incorporates self-attention modules to better capture long-range dependencies and jointly optimizes structural similarity and gradient variance losses to enhance anatomical fidelity and fine detail preservation. To support supervised training, we construct a high-quality paired dataset of camera and cSLO images collected from the same patients, with all pairs manually aligned and clinically verified to ensure diagnostic relevance. Experimental results demonstrate that our method achieves state-of-the-art performance in both perceptual realism and structural accuracy. Additionally, we introduce the Feature Matching Success Rate (FMSR), a novel keypoint-based metric using AKAZE descriptors, to quantitatively assess anatomical consistency across modalities. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviews received at journal 26 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers invited by journal 08 Aug, 2025 Editor assigned by journal 31 Jul, 2025 Submission checks completed at journal 30 Jul, 2025 First submitted to journal 30 Jul, 2025 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. 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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-7229849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":498028202,"identity":"bd79213d-8469-4cd5-ab2b-99f99726738d","order_by":0,"name":"Jaehan Joo","email":"","orcid":"","institution":"Pusan National University","correspondingAuthor":false,"prefix":"","firstName":"Jaehan","middleName":"","lastName":"Joo","suffix":""},{"id":498028203,"identity":"01265feb-3c9e-4f73-a4b0-bf60e130d150","order_by":1,"name":"Ji-Eun Lee","email":"","orcid":"","institution":"Pusan National University Yangsan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ji-Eun","middleName":"","lastName":"Lee","suffix":""},{"id":498028204,"identity":"42339c4f-60a4-4d92-b345-89518cdce5fa","order_by":2,"name":"Su-Jin Kim","email":"","orcid":"","institution":"Pusan National University Yangsan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Su-Jin","middleName":"","lastName":"Kim","suffix":""},{"id":498028205,"identity":"9ac38d2b-0592-470c-8b7f-fff5b8f91d2c","order_by":3,"name":"Seung Min Lee","email":"","orcid":"","institution":"Pusan National University Yangsan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Seung","middleName":"Min","lastName":"Lee","suffix":""},{"id":498028206,"identity":"19ec6554-28f8-4124-a44d-dc5da188de73","order_by":4,"name":"Suk Chan Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYFACNoYDDBUIrgGRWs6QqoWBsY0ULfLtxxIP3Zx3J3F++9kDDD9qGIzNGwhoMTiTduBw7rZniRvO5CUw9hxjMJM5QEiLBHsDUMvhxA0MOQYMvA0MNhIEHTYDpGXO4cT5/W8MGP8So4XhBhvQYQ2HExtu5BgwA20xI6gF6JeEwznHnhlvuPHG4LDMMQljwg5rP2b8Oafmjuz8/hzDh29qbAxnEHQYBByAkYR9gqplFIyCUTAKRgFWAADmkkM03ZedyAAAAABJRU5ErkJggg==","orcid":"","institution":"Pusan National University","correspondingAuthor":true,"prefix":"","firstName":"Suk","middleName":"Chan","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-07-28 05:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7229849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7229849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88939649,"identity":"9c8580f2-60d3-4095-a419-445eb4af99be","added_by":"auto","created_at":"2025-08-13 02:54:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1699967,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7229849/v1_covered_c8f9f017-2e4c-43d8-9602-beebbc8875a2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Generative Model-Based Fundus Photography Translation for Enhanced Cross-Device Consistency","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7229849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7229849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We propose a novel image translation framework that converts fundus images from conventional fundus cameras to confocal scanning laser ophthalmoscopy (cSLO), aiming to bridge a clinically significant domain gap that has been largely overlooked. 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