Semantic-Enhanced Few-Shot Learning for Precise Dermatological Diagnosis

Semantic-Enhanced Few-Shot Learning for Precise Dermatological Diagnosis | 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 Semantic-Enhanced Few-Shot Learning for Precise Dermatological Diagnosis Yuxiang Wang, Jinrong He, Shuai Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8156285/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Apr, 2026 Read the published version in The Visual Computer → Version 1 posted 10 You are reading this latest preprint version Abstract Accurate diagnosis of dermatological diseases remains challenging due to fine-grained inter-class similarities and high intra-class variability. Few-shot learning (FSL) offers a promising solution, yet existing methods often fail to capture subtle lesion characteristics. We propose the Semantic Feature Enhanced Prototypical Transformer (SFEPT), a unified framework for few-shot dermatological image classification. SFEPT employs a Swin Transformer backbone to extract hierarchical features and a semantic-aware enhancement module to enrich query representations. Additionally, a prototype rectification strategy refines class prototypes, enhancing robustness. Our evaluations on three dermatological benchmarks demonstrate superior performance, achieving 89.53% and 81.85% accuracy on SD-198 and Derm7pt, respectively, in 3-way 5-shot tasks. The code is available at https://github.com/AIYAU/FSL_dermatopathya . Few-shot learning Dermatological image classification Semantic feature enhancement Bias diminishing Fine-grained classification Swin-transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Apr, 2026 Read the published version in The Visual Computer → Version 1 posted Editorial decision: Revision requested 19 Jan, 2026 Reviews received at journal 30 Dec, 2025 Reviewers agreed at journal 30 Dec, 2025 Reviewers agreed at journal 28 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviewers invited by journal 30 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 19 Nov, 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. 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-8156285","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553427812,"identity":"363f8868-1505-4106-b991-11c1e4eb9b0b","order_by":0,"name":"Yuxiang Wang","email":"","orcid":"","institution":"Yan'an University","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Wang","suffix":""},{"id":553427813,"identity":"8192fc3a-6a33-4b50-bc40-90bfb4db8104","order_by":1,"name":"Jinrong 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Few-shot learning (FSL) offers a promising solution, yet existing methods often fail to capture subtle lesion characteristics. We propose the Semantic Feature Enhanced Prototypical Transformer (SFEPT), a unified framework for few-shot dermatological image classification. SFEPT employs a Swin Transformer backbone to extract hierarchical features and a semantic-aware enhancement module to enrich query representations. Additionally, a prototype rectification strategy refines class prototypes, enhancing robustness. Our evaluations on three dermatological benchmarks demonstrate superior performance, achieving 89.53% and 81.85% accuracy on SD-198 and Derm7pt, respectively, in 3-way 5-shot tasks. The code is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/AIYAU/FSL_dermatopathya\u003c/span\u003e\u003cspan address=\"https://github.com/AIYAU/FSL_dermatopathya\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e","manuscriptTitle":"Semantic-Enhanced Few-Shot Learning for Precise Dermatological Diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 05:38:34","doi":"10.21203/rs.3.rs-8156285/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-19T15:13:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-31T03:03:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320164564078253830271737434856670379773","date":"2025-12-31T02:27:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303967756651839345044770672862857662191","date":"2025-12-28T10:27:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T02:53:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290514293062872453161483803064600981555","date":"2025-12-01T02:30:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T01:33:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T13:44:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T08:47:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Visual Computer","date":"2025-11-19T14:07:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-visual-computer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"tvcj","sideBox":"Learn more about [The Visual Computer](http://link.springer.com/journal/371)","snPcode":"371","submissionUrl":"https://submission.nature.com/new-submission/371/3","title":"The Visual Computer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b493084f-a5f2-499d-92ee-e16f3aab5d43","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:15:41+00:00","versionOfRecord":{"articleIdentity":"rs-8156285","link":"https://doi.org/10.1007/s00371-026-04440-y","journal":{"identity":"the-visual-computer","isVorOnly":false,"title":"The Visual Computer"},"publishedOn":"2026-04-08 15:58:49","publishedOnDateReadable":"April 8th, 2026"},"versionCreatedAt":"2025-12-02 05:38:34","video":"","vorDoi":"10.1007/s00371-026-04440-y","vorDoiUrl":"https://doi.org/10.1007/s00371-026-04440-y","workflowStages":[]},"version":"v1","identity":"rs-8156285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8156285","identity":"rs-8156285","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}