DynamicBUS: Restoring Temporal Dynamics from Static Ultrasound for Improved Breast Cancer Diagnosis

DynamicBUS: Restoring Temporal Dynamics from Static Ultrasound for Improved Breast Cancer 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 DynamicBUS: Restoring Temporal Dynamics from Static Ultrasound for Improved Breast Cancer Diagnosis Zhikai Yang, Yaofang Liu, Tianhao Bai, Ander Biguri, Haoyuan Chen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8666556/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The clinical practice of archiving static 2D images from dynamic breast ultrasound (BUS) examinations leads to a critical loss of temporal information, vital for accurate lesion diagnosis. This data limitation constrains the performance of conventional computer-aided diagnosis systems. We challenge this limitation by proposing a novel framework that computationally recovers and leverages these lost temporal dynamics to enhance diagnostic accuracy. Our framework first synthesizes a BUS video from a static key frame image using a purpose-built generative model. For diagnostically plausible synthesis, we introduce a key-frame conditioning strategy that ensures the anatomical fidelity of the lesion is preserved while generating useful dynamic cues. Subsequently, the high-fidelity static image and the synthesized video are fed into our designed IV-Net, a dual-branch fusion network that synergistically integrates pristine spatial details with the recovered temporal context for robust classification. Evaluated on key frame BUS datasets, our integrated framework outperforms methods that rely solely on static images (AUC: 92.63%).Moreover, a reader study indicates that the generated videos are indistinguishable to experts and lead to higher diagnostic performance. Overall, our method demonstrates the potential of generative AI to restore lost clinical information, paving the way for more accurate and reliable diagnostic systems in BUS diagnosis. The code and generated dataset are publicly available at https://github.com/minnelab/DynamicBUS Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Video Generation Breast Ultrasound Spatio-Temporal Fusion Diffusion Models Computer-Aided Diagnosis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviews received at journal 12 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers invited by journal 04 Feb, 2026 Editor assigned by journal 02 Feb, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 22 Jan, 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-8666556","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":585889636,"identity":"1f8bcfc3-b96b-42b7-8d84-d72632f11dd2","order_by":0,"name":"Zhikai Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYDACCcZmBgYDGwMGCRK1pBnwkKCFgRlIHiZBi/zs5maDHwXnje2lG5g/fCBGC+Ocg82JPQa3zXhkDrBJziBGC7NEYvMBHoPbNjwSCWzMPMRoYQNqOfjH4BxIC/PnP8Ro4QFqSeYxOGAG1MIgTYwOBgmgFmMZg2RjnhuJbZI9xGiRn5H+WPLNHzvD9hnJhz/8IMoaBGBsIFHDKBgFo2AUjAKcAACccSvcIylt4QAAAABJRU5ErkJggg==","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhikai","middleName":"","lastName":"Yang","suffix":""},{"id":585889637,"identity":"0de2e308-1657-4739-98ef-4dc0e4302ca4","order_by":1,"name":"Yaofang Liu","email":"","orcid":"","institution":"City University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Yaofang","middleName":"","lastName":"Liu","suffix":""},{"id":585889638,"identity":"134eaf5c-02f1-4733-afd5-aa94259d631d","order_by":2,"name":"Tianhao Bai","email":"","orcid":"","institution":"Aviation General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianhao","middleName":"","lastName":"Bai","suffix":""},{"id":585889639,"identity":"361001e0-8b49-4b32-afd8-106075a9737e","order_by":3,"name":"Ander Biguri","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Ander","middleName":"","lastName":"Biguri","suffix":""},{"id":585889640,"identity":"3872bc0d-d22b-4f50-ae1f-54d18b1c90c6","order_by":4,"name":"Haoyuan Chen","email":"","orcid":"","institution":"ShanghaiTech University","correspondingAuthor":false,"prefix":"","firstName":"Haoyuan","middleName":"","lastName":"Chen","suffix":""},{"id":585889641,"identity":"493c691e-2ba5-4952-8755-c0a4251c5c01","order_by":5,"name":"Yonghao Li","email":"","orcid":"","institution":"ShanghaiTech University","correspondingAuthor":false,"prefix":"","firstName":"Yonghao","middleName":"","lastName":"Li","suffix":""},{"id":585889642,"identity":"1798c136-be57-44a4-9dcf-331d4aec5c0f","order_by":6,"name":"Carola-Bibiane Schönlieb","email":"","orcid":"","institution":"University of Cambridge","correspondingAuthor":false,"prefix":"","firstName":"Carola-Bibiane","middleName":"","lastName":"Schönlieb","suffix":""},{"id":585889643,"identity":"0eb8568a-a1d8-4bf2-9983-944df8c44529","order_by":7,"name":"Örjan Smedby","email":"","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Örjan","middleName":"","lastName":"Smedby","suffix":""},{"id":585889644,"identity":"1c48a825-fcdd-443e-b091-47c551fea62d","order_by":8,"name":"Raymond Chan","email":"","orcid":"","institution":"City University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Raymond","middleName":"","lastName":"Chan","suffix":""},{"id":585889645,"identity":"68eb014c-b92d-4d42-af3c-0393b96ad9a3","order_by":9,"name":"Rodrigo Moreno","email":"","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Moreno","suffix":""}],"badges":[],"createdAt":"2026-01-22 07:34:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8666556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8666556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102297588,"identity":"50145d28-2c78-45f1-a0b2-6da42c24f1b3","added_by":"auto","created_at":"2026-02-10 10:28:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1683438,"visible":true,"origin":"","legend":"","description":"","filename":"BUSDynamicnotemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8666556/v1_covered_7ecef497-ff73-4f2e-95ed-3a5c61cae814.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DynamicBUS: Restoring Temporal Dynamics from Static Ultrasound for Improved Breast Cancer 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":"npj-breast-cancer","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjbcancer","sideBox":"Learn more about [npj Breast Cancer](http://www.nature.com/npjbcancer/)","snPcode":"41523","submissionUrl":"https://mts-npjbcancer.nature.com/","title":"npj Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Video Generation, Breast Ultrasound, Spatio-Temporal Fusion, Diffusion Models, Computer-Aided Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8666556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8666556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The clinical practice of archiving static 2D images from dynamic breast ultrasound (BUS) examinations leads to a critical loss of temporal information, vital for accurate lesion diagnosis. This data limitation constrains the performance of conventional computer-aided diagnosis systems. We challenge this limitation by proposing a novel framework that computationally recovers and leverages these lost temporal dynamics to enhance diagnostic accuracy. Our framework first synthesizes a BUS video from a static key frame image using a purpose-built generative model. For diagnostically plausible synthesis, we introduce a key-frame conditioning strategy that ensures the anatomical fidelity of the lesion is preserved while generating useful dynamic cues. Subsequently, the high-fidelity static image and the synthesized video are fed into our designed IV-Net, a dual-branch fusion network that synergistically integrates pristine spatial details with the recovered temporal context for robust classification. Evaluated on key frame BUS datasets, our integrated framework outperforms methods that rely solely on static images (AUC: 92.63\\%).Moreover, a reader study indicates that the generated videos are indistinguishable to experts and lead to higher diagnostic performance. Overall, our method demonstrates the potential of generative AI to restore lost clinical information, paving the way for more accurate and reliable diagnostic systems in BUS diagnosis. The code and generated dataset are publicly available at https://github.com/minnelab/DynamicBUS","manuscriptTitle":"DynamicBUS: Restoring Temporal Dynamics from Static Ultrasound for Improved Breast Cancer Diagnosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-09 13:34:02","doi":"10.21203/rs.3.rs-8666556/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-13T03:31:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56533990135137093560921271874182258576","date":"2026-05-05T06:30:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226740755234617388785635878899764444823","date":"2026-05-04T08:28:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250161024837707423908548202613765927786","date":"2026-05-01T06:25:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270472869871166475673765467549605359986","date":"2026-03-09T14:25:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-12T21:43:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149605162704279401097903437303808799847","date":"2026-02-04T18:02:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-04T17:59:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-02T09:53:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-27T07:31:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Breast Cancer","date":"2026-01-22T07:21:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-breast-cancer","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjbcancer","sideBox":"Learn more about [npj Breast Cancer](http://www.nature.com/npjbcancer/)","snPcode":"41523","submissionUrl":"https://mts-npjbcancer.nature.com/","title":"npj Breast Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1527a5a8-5dda-406a-94b3-5fa860def7dc","owner":[],"postedDate":"February 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-13T03:31:13+00:00","index":29,"fulltext":""},{"type":"reviewerAgreed","content":"56533990135137093560921271874182258576","date":"2026-05-05T06:30:17+00:00","index":28,"fulltext":""},{"type":"reviewerAgreed","content":"226740755234617388785635878899764444823","date":"2026-05-04T08:28:41+00:00","index":27,"fulltext":""},{"type":"reviewerAgreed","content":"250161024837707423908548202613765927786","date":"2026-05-01T06:25:08+00:00","index":26,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62335678,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":62335679,"name":"Physical sciences/Engineering"},{"id":62335680,"name":"Health sciences/Health care"},{"id":62335681,"name":"Physical sciences/Mathematics and computing"},{"id":62335682,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-02-09T13:34:02+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-09 13:34:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8666556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8666556","identity":"rs-8666556","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}