Exploiting Shared and Distinctive Representations for Enhanced Multi-Modality Medical Image Analysis

Exploiting Shared and Distinctive Representations for Enhanced Multi-Modality Medical Image Analysis | 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 Exploiting Shared and Distinctive Representations for Enhanced Multi-Modality Medical Image Analysis Zhixian Wang, Tao Zhang, Wu Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4346867/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Multi-modality medical image classification aims to combine information from different modalities or devices to generate comprehensive and accurate diagnostic results. Existing research methods have ignored two characteristics of medical images across different phases: the highly redundant background and may exist the low differentiation between different phases. Based on the idea of disentangled representation learning, we introduce a dual-branch network to disentangle images into shared features and modality-specific features. And based on the properties of different features, we propose a prototypical loss and a similar prototypical loss to constrain the two types of features, respectively. Our approach achieves strong performance in classification on LLD-MMRI dataset and fusion on ANNLIB dataset. Extensive ablation studies validate the contribution of each component of our framework. Medical Image Fusion Multi-Modal Classification Unify Framework Dual-Branch Prototype Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4346867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299557779,"identity":"935de8d7-1de3-450d-82a9-10b9b21fd71a","order_by":0,"name":"Zhixian Wang","email":"","orcid":"","institution":"Chengdu Techman Software Co.","correspondingAuthor":false,"prefix":"","firstName":"Zhixian","middleName":"","lastName":"Wang","suffix":""},{"id":299557780,"identity":"bc393e61-ecef-4f3f-8d8d-48b41f7ad9ce","order_by":1,"name":"Tao Zhang","email":"","orcid":"","institution":"Chengdu Techman Software Co.","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""},{"id":299557781,"identity":"6bf42df6-2fe9-4e02-a32c-d0e1fb50b378","order_by":2,"name":"Wu Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACAygtB6HYSNBiTLqWxAaitZhLJD97+LXtcPr89jMGDB/KDjPwz27Ar8VyRpq5sWzb4dwNZ3IMGGecO8wgcecAAYfdSDCTltwG1CLBY8DM23aYwUAigZCW9G8gLenyM4Ba/hKnJcdM8uO2wwkMN4BaGInScuZNmTTjv3TDDWfSCg72nEvnkbhBSMvx9G2SP85Yy8u3H9744EeZtRz/DAJaQICZB8o4AMQ8eBQiAOMPopSNglEwCkbBiAUApDBCqeE/SnQAAAAASUVORK5CYII=","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Wu","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2024-04-30 07:00:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4346867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4346867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57308803,"identity":"188c7c92-cdd8-478e-a20a-3a0a3b70a683","added_by":"auto","created_at":"2024-05-29 02:38:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5615228,"visible":true,"origin":"","legend":"","description":"","filename":"mvap2024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4346867/v1_covered_208e52f3-158e-4df0-81bd-4661540b109c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploiting Shared and Distinctive Representations for Enhanced Multi-Modality Medical Image Analysis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medical Image Fusion, Multi-Modal Classification, Unify Framework, Dual-Branch, Prototype","lastPublishedDoi":"10.21203/rs.3.rs-4346867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4346867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Multi-modality medical image classification aims to combine information from different modalities or devices to generate comprehensive and accurate diagnostic results. Existing research methods have ignored two characteristics of medical images across different phases: the highly redundant background and may exist the low differentiation between different phases. Based on the idea of disentangled representation learning, we introduce a dual-branch network to disentangle images into shared features and modality-specific features. And based on the properties of different features, we propose a prototypical loss and a similar prototypical loss to constrain the two types of features, respectively. Our approach achieves strong performance in classification on LLD-MMRI dataset and fusion on ANNLIB dataset. Extensive ablation studies validate the contribution of each component of our framework.","manuscriptTitle":"Exploiting Shared and Distinctive Representations for Enhanced Multi-Modality Medical Image Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 19:48:15","doi":"10.21203/rs.3.rs-4346867/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"873b53a0-2074-4d2a-a12a-86d4b82dd7ca","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-29T02:30:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-08 19:48:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4346867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4346867","identity":"rs-4346867","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}