CMDB Net: Multimodal De-Biased Fusion Network for Robust Dermatological Diagnosis

CMDB Net: Multimodal De-Biased Fusion Network for Robust 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 Article CMDB Net: Multimodal De-Biased Fusion Network for Robust Dermatological Diagnosis Tongtong Che, Feng Li, Min Li, Xin Chen, Lei Chu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9179471/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Effective clinical decision support in dermatology requires AI systems that reliably integrate multimodal patient data—including clinical metadata and lesion images, while mitigating modal bias that compromises diagnostic fairness and robustness across diverse patient populations. We propose the Case Mining-Based De-Biased Fusion Network (CMDB Net) to systematically address modality bias in multimodal dermatological diagnosis, advancing clinical decision support toward fairness and trustworthiness. CMDB Net combines supervised contrastive learning for robust cross-modal case feature mining with a dual-branch cross-modal attention mechanism regularized by Jensen–Shannon divergence to dynamically balance modality contributions and suppress feature bias. Extensive evaluation on PAD-UFES-20 and Derm7pt datasets demonstrates significant improvements in balanced accuracy (4.0% and 10.6% respectively), indicating enhanced diagnostic fairness for under-represented disease classes. CMDB Net achieves 86.2% accuracy on PAD-UFES-20 while maintaining superior and consistent performance across multiple backbone architectures. By systematically mitigating modal bias, CMDB Net improves both diagnostic accuracy and fairness, providing a trustworthy and task-agnostic framework for clinical decision support deployable in diverse settings, from centralized medical centers to resource-limited clinical environments. The code for this study is publicly accessible at https://github.com/lifeng-medical/CMDB-Master . Biological sciences/Computational biology and bioinformatics Health sciences/Diseases 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 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Editor invited by journal 07 Apr, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 28 Mar, 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. 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-9179471","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":630278578,"identity":"5fbeac85-39a3-4bff-9039-8b423351551d","order_by":0,"name":"Tongtong Che","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Tongtong","middleName":"","lastName":"Che","suffix":""},{"id":630278579,"identity":"97928eed-d5b0-4889-982a-1e7704505c76","order_by":1,"name":"Feng Li","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Li","suffix":""},{"id":630278580,"identity":"3dcc9b88-2e11-4a40-aeaf-a851749cbf52","order_by":2,"name":"Min Li","email":"","orcid":"","institution":"The First People’s Hospital of Kashi","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":630278582,"identity":"372f2c3d-01e6-4243-b024-0adeeac6bc73","order_by":3,"name":"Xin Chen","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""},{"id":630278583,"identity":"3101c53f-0c84-4856-a595-a1d9e4256e5b","order_by":4,"name":"Lei Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCQbGAwwGEnL8DIcPALkWDAw8hLUwALVYGEs2HktgYEiQIFYLQ0XihsNnDIjTwj+7x+DAhwKJxA3Hznx8zPsD6EKeA4wfPubgseTOGYODMwwkjGeeObvZmCdBwliyt4FZcuY23FoMJHIMDvMYSMj23Ti7TRqoJXHDeQY2Zl5CWv4YSDA23H/zjAQtQFJxwoEzbBAtZxvwa5G4kVZwsAfoF8mGY8aGc9KAjJ6DzXj9wj8jeeODH3/qQFH58MEbGxtgiCUf/PARjxZsgLGBNPWjYBSMglEwCjAAAF3zU0OO3lgdAAAAAElFTkSuQmCC","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2026-03-20 13:39:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9179471/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9179471/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182004,"identity":"7bab19f5-ccdf-40a4-a42e-1d62eaf0aa1b","added_by":"auto","created_at":"2026-04-30 08:59:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":818711,"visible":true,"origin":"","legend":"","description":"","filename":"CMDBtoScientificReportsLatex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9179471/v1_covered_a21d0cc0-6515-400a-86c2-aeebbce6e768.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CMDB Net: Multimodal De-Biased Fusion Network for Robust Dermatological Diagnosis","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-9179471/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9179471/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Effective clinical decision support in dermatology requires AI systems that reliably integrate multimodal patient data—including clinical metadata and lesion images, while mitigating modal bias that compromises diagnostic fairness and robustness across diverse patient populations. We propose the Case Mining-Based De-Biased Fusion Network (CMDB Net) to systematically address modality bias in multimodal dermatological diagnosis, advancing clinical decision support toward fairness and trustworthiness. CMDB Net combines supervised contrastive learning for robust cross-modal case feature mining with a dual-branch cross-modal attention mechanism regularized by Jensen–Shannon divergence to dynamically balance modality contributions and suppress feature bias. Extensive evaluation on PAD-UFES-20 and Derm7pt datasets demonstrates significant improvements in balanced accuracy (4.0% and 10.6% respectively), indicating enhanced diagnostic fairness for under-represented disease classes. CMDB Net achieves 86.2% accuracy on PAD-UFES-20 while maintaining superior and consistent performance across multiple backbone architectures. By systematically mitigating modal bias, CMDB Net improves both diagnostic accuracy and fairness, providing a trustworthy and task-agnostic framework for clinical decision support deployable in diverse settings, from centralized medical centers to resource-limited clinical environments. 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