TVNet: Multimodal Medical Image Fusion by Dual-branch Network with Vision Transformer and One-Shot Aggregation Module

TVNet: Multimodal Medical Image Fusion by Dual-branch Network with Vision Transformer and One-Shot Aggregation Module | 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 TVNet: Multimodal Medical Image Fusion by Dual-branch Network with Vision Transformer and One-Shot Aggregation Module Jianguo Wang, Wenran Jia, Peng Geng, Pengfei Wu, Yuting Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4759723/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 The task of medical image fusion is to synthesize complementary information from different modals medical images, which is of very significance for clinical diagnosis. The existing medical image fusion algorithms overly rely on convolution operations and cannot establish long-range dependencies on the source images. This can lead to edge blurring and loss of details in the fused images. Because the Transformer can effectively model long-range dependencies through self-attention, a novel and effective dual-branch feature enhancement network called as TVNet is proposed to fusing multimodal medical images. This network combines Transformer and CNN to extract global context information and local information to preserve detailed textures and highlight the structural characteristics in source images. Furthermore, to extract the multi-scale information of images, an enhancement module is used to obtain multi-scale characterization information, and the two branches information are efficiently aggregated at the same time. In addition, a hybrid loss function is designed to optimize the fusion results at three levels of structure, feature and gradient. Experiments results prove that the performance of the proposed fusion network outperforms seven state-of-the-art methods in both subjective visual effects and objective metrics. Medical image fusion Convolution neural network Vision Transformer Long-range dependencies Multi-scale features 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. 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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-4759723","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338372414,"identity":"d17e9add-a80d-441e-a075-ae38198dd777","order_by":0,"name":"Jianguo Wang","email":"","orcid":"","institution":"Beijing Information Technology College","correspondingAuthor":false,"prefix":"","firstName":"Jianguo","middleName":"","lastName":"Wang","suffix":""},{"id":338372416,"identity":"e611ae3e-daab-496b-b801-44fa858ff7ad","order_by":1,"name":"Wenran Jia","email":"","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":false,"prefix":"","firstName":"Wenran","middleName":"","lastName":"Jia","suffix":""},{"id":338372418,"identity":"d6bc4a22-cd40-4b48-8616-df0990da3166","order_by":2,"name":"Peng Geng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBAC/gYYA4glgJixAadaKJA4AGVINjATqcUAzjhAtBb23sOvCyru2G0+f/7gbR4GG9kNB5ifPcCrhedcmvWMM8+St91IZrbmYUgz3nCAzdwAnxbDGzlmxrxth5PNbjCzSfMwHE7ccICHTQKvLfffALX8O5xs3H8YpOU/EVpu8Bg/5m04bGfAkAzScoCwFokzOWbMM44dTpC4kWxsOccg2XjmYTYzvFr4288Yfy6oOWzP33/w4Y03FXayfcebn+HVAgRs0kAisQHiTiBmJqAepOQzkLAnrG4UjIJRMApGLAAAGiFJeOLQhawAAAAASUVORK5CYII=","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Geng","suffix":""},{"id":338372419,"identity":"796c1750-8c54-48fb-af1b-560cdfb31209","order_by":3,"name":"Pengfei Wu","email":"","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Wu","suffix":""},{"id":338372420,"identity":"a21111f9-3a10-45e1-97c0-707801ed3feb","order_by":4,"name":"Yuting Zhang","email":"","orcid":"","institution":"Shijiazhuang Tiedao University","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Zhang","suffix":""},{"id":338372421,"identity":"167ccfb5-3863-4976-b421-3cca5da420c3","order_by":5,"name":"Xuguang Meng","email":"","orcid":"","institution":"Hebei Information and Industry Technology Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xuguang","middleName":"","lastName":"Meng","suffix":""}],"badges":[],"createdAt":"2024-07-18 03:50:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4759723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4759723/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62372737,"identity":"a53743ed-ef13-4c6b-8d02-f0afcfd2068c","added_by":"auto","created_at":"2024-08-13 12:34:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886935,"visible":true,"origin":"","legend":"","description":"","filename":"SignalImageandVideoProcessing3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4759723/v1_covered_dae4c0b5-860e-404b-bbfa-c5e05991e355.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TVNet: Multimodal Medical Image Fusion by Dual-branch Network with Vision Transformer and One-Shot Aggregation Module","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, Convolution neural network, Vision Transformer, Long-range dependencies, Multi-scale features","lastPublishedDoi":"10.21203/rs.3.rs-4759723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4759723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The task of medical image fusion is to synthesize complementary information from different modals medical images, which is of very significance for clinical diagnosis. 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