The application of multi-instance classification technology based on microscopic hyperspectral imaging in the analysis of microscopic hyperspectral breast cancer dataset

The application of multi-instance classification technology based on microscopic hyperspectral imaging in the analysis of microscopic hyperspectral breast cancer dataset | 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 The application of multi-instance classification technology based on microscopic hyperspectral imaging in the analysis of microscopic hyperspectral breast cancer dataset Zhuowei Chen, Qingyu Yang, Geng Qin, Xiaoying Ma, Zhuo Lu, Haiyan Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7636945/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 Microscopic hyperspectral imaging (MHSI) of unstained tissue offers a promising, quantitative alternative to traditional histopathology but is challenged by low-contrast morphology and high-dimensional data. Traditional pathological diagnosis, when applied to unstained tissue sections, often relies on the analysis of individual patches. The fatal flaw in this approach, however, lies in the inherent low contrast of unstained sections. This can render any single patch informationally ambiguous, making it difficult for models to accurately differentiate between paracancerous and tumor tissue. To fundamentally address this issue, we propose a new diagnostic paradigm: to shift away from relying on unreliable individual patches and instead perform a holistic diagnosis by intelligently integrating information from all patches across an entire slide. Based on this principle, we designed the Multi-Scale Hierarchical Attention Network (MS-HAN). The core advantage of MS-HAN lies in its hierarchical integration mechanism. It begins by extracting features from each independent patch, but critically, it refrains from making judgments based on any single one. Instead, it employs a multi-head self-attention aggregator to evaluate the importance of all patches within the slide, fusing them into a weighted, global feature vector that represents the slide's overall state. This design enables the model to automatically disregard uninformative or misleading patches and focus its attention on the most diagnostically critical regions.We validated the superiority of this method on a dataset from 60 breast cancer patients. By integrating whole-slide information, MS-HAN achieved an accuracy of 86.2% and an AUC of 0.91. Furthermore, interpretability analysis confirmed that the model makes its final prediction precisely by identifying and focusing on key diagnostic regions within the slide. These results demonstrate that our integrated diagnostic framework successfully overcomes the limitations of single-patch dependency, offering a more robust and precise solution for stain-free computational pathology. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Health sciences/Oncology 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-7636945","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":528737977,"identity":"22995663-e08d-4090-a7c5-f4ff0834d7d5","order_by":0,"name":"Zhuowei Chen","email":"","orcid":"","institution":"City University of Macau","correspondingAuthor":false,"prefix":"","firstName":"Zhuowei","middleName":"","lastName":"Chen","suffix":""},{"id":528737978,"identity":"902fe672-2bdd-4de4-80ab-58568bb10360","order_by":1,"name":"Qingyu Yang","email":"","orcid":"","institution":"The Sixth Affiliated Hospital, Sun Yat-sen 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dataset","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 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Traditional pathological diagnosis, when applied to unstained tissue sections, often relies on the analysis of individual patches. The fatal flaw in this approach, however, lies in the inherent low contrast of unstained sections. This can render any single patch informationally ambiguous, making it difficult for models to accurately differentiate between paracancerous and tumor tissue. To fundamentally address this issue, we propose a new diagnostic paradigm: to shift away from relying on unreliable individual patches and instead perform a holistic diagnosis by intelligently integrating information from all patches across an entire slide. Based on this principle, we designed the Multi-Scale Hierarchical Attention Network (MS-HAN). The core advantage of MS-HAN lies in its hierarchical integration mechanism. It begins by extracting features from each independent patch, but critically, it refrains from making judgments based on any single one. Instead, it employs a multi-head self-attention aggregator to evaluate the importance of all patches within the slide, fusing them into a weighted, global feature vector that represents the slide's overall state. This design enables the model to automatically disregard uninformative or misleading patches and focus its attention on the most diagnostically critical regions.We validated the superiority of this method on a dataset from 60 breast cancer patients. By integrating whole-slide information, MS-HAN achieved an accuracy of 86.2\\% and an AUC of 0.91. Furthermore, interpretability analysis confirmed that the model makes its final prediction precisely by identifying and focusing on key diagnostic regions within the slide. These results demonstrate that our integrated diagnostic framework successfully overcomes the limitations of single-patch dependency, offering a more robust and precise solution for stain-free computational pathology.","manuscriptTitle":"The application of multi-instance classification technology based on microscopic hyperspectral imaging in the analysis of microscopic hyperspectral breast cancer dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 05:47:31","doi":"10.21203/rs.3.rs-7636945/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":"e06e0633-6cce-40dd-9852-62ba158243be","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56193968,"name":"Biological sciences/Cancer"},{"id":56193969,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56193970,"name":"Physical sciences/Engineering"},{"id":56193971,"name":"Physical sciences/Mathematics and computing"},{"id":56193972,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-11-12T13:24:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 05:47:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7636945","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7636945","identity":"rs-7636945","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}