Deep Learning–Based Approach for Quality Control Scoring of Digital Pathological Sections

Deep Learning–Based Approach for Quality Control Scoring of Digital Pathological Sections | 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 Deep Learning–Based Approach for Quality Control Scoring of Digital Pathological Sections qingya luo, yanjun chen, na zhao, xueyuan zhang, xiaowen wang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8087983/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Apr, 2026 Read the published version in European Journal of Medical Research → Version 1 posted 17 You are reading this latest preprint version Abstract Objective To explore the auxiliary role and application of deep learning-based artificial intelligence (AI) in quality control (QC) evaluation of digital pathological section. Methods A total of 2137 routine hematoxylin and eosin (HE) slides from Department of Pathology, the First Affiliated Hospital of Army Medical University, collected between January and December 2022, were scanned into digital slides. Based on slide evaluation standards, these digital slides were scored into four grades: A, B, C, and D. ResNet50, ResNet101, EfficientNet-B5, and Swin Transformer networks were then employed for classification learning. During model training, parameters trained on the ImageNet dataset were used as initial values, and QC data were utilized to perform secondary training and optimization of the models. A set of 429 routine HE slides was selected for model validation and deep learning. Results Among the four classification models, Swin Transformer achieved the best performance for all grades except for grade D, where ResNet50 performed optimally. Overall, the Swin Transformer demonstrated the highest performance with an accuracy rate of 0.83 and an Area Under the Curve (AUC) value of 0.88. The prediction speed reached 0.6 seconds per slide. Conclusion This study preliminarily validates that a deep learning-based auxiliary QC scoring system built on Swin Transformer performs well in terms of accuracy and timeliness for slide QC evaluation. This method can significantly enhance the efficiency of pathology slide QC and plays an important role in the development of an informative pathology department. Deep learning Digital pathological section Pathology techniques Quality control Artificial intelligence Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.doc Cite Share Download PDF Status: Published Journal Publication published 20 Apr, 2026 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 24 Feb, 2026 Reviews received at journal 03 Jan, 2026 Reviews received at journal 29 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 19 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers agreed at journal 16 Dec, 2025 Reviewers invited by journal 16 Dec, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 18 Nov, 2025 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. 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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-8087983","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561498595,"identity":"6ddbec40-6b77-464c-849e-209c36598b19","order_by":0,"name":"qingya luo","email":"","orcid":"","institution":"southwest hospital","correspondingAuthor":false,"prefix":"","firstName":"qingya","middleName":"","lastName":"luo","suffix":""},{"id":561498598,"identity":"1dd520cd-a746-4b7f-b000-463c35891f03","order_by":1,"name":"yanjun chen","email":"","orcid":"","institution":"southwest 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Sections","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Deep learning, Digital pathological section, Pathology techniques, Quality control, Artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-8087983/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8087983/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo explore the auxiliary role and application of deep learning-based artificial intelligence (AI) in quality control (QC) evaluation of digital pathological section.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 2137 routine hematoxylin and eosin (HE) slides from Department of Pathology, the First Affiliated Hospital of Army Medical University, collected between January and December 2022, were scanned into digital slides. Based on slide evaluation standards, these digital slides were scored into four grades: A, B, C, and D. ResNet50, ResNet101, EfficientNet-B5, and Swin Transformer networks were then employed for classification learning. During model training, parameters trained on the ImageNet dataset were used as initial values, and QC data were utilized to perform secondary training and optimization of the models. A set of 429 routine HE slides was selected for model validation and deep learning.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the four classification models, Swin Transformer achieved the best performance for all grades except for grade D, where ResNet50 performed optimally. Overall, the Swin Transformer demonstrated the highest performance with an accuracy rate of 0.83 and an Area Under the Curve (AUC) value of 0.88. The prediction speed reached 0.6 seconds per slide.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study preliminarily validates that a deep learning-based auxiliary QC scoring system built on Swin Transformer performs well in terms of accuracy and timeliness for slide QC evaluation. This method can significantly enhance the efficiency of pathology slide QC and plays an important role in the development of an informative pathology department.\u003c/p\u003e","manuscriptTitle":"Deep Learning–Based Approach for Quality Control Scoring of Digital Pathological Sections","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-18 11:49:06","doi":"10.21203/rs.3.rs-8087983/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-24T05:56:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T17:36:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-29T17:53:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T14:23:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T12:53:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22049617262990258877233626938628324748","date":"2025-12-19T18:58:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180127240702592762443833381332236549622","date":"2025-12-18T15:01:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62541587905227819458839699100459808142","date":"2025-12-17T03:41:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T15:34:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263325409012852295871642148018637490427","date":"2025-12-16T14:41:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T12:47:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275182630242315628327629865832734164026","date":"2025-12-16T12:47:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114825721802104483663319575671469779957","date":"2025-12-16T12:45:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-16T12:35:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-20T16:59:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T16:33:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-11-18T12:46:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff5826ba-97c7-46b0-8c21-3aa4d356039a","owner":[],"postedDate":"December 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:04:57+00:00","versionOfRecord":{"articleIdentity":"rs-8087983","link":"https://doi.org/10.1186/s40001-026-04393-x","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2026-04-20 15:58:21","publishedOnDateReadable":"April 20th, 2026"},"versionCreatedAt":"2025-12-18 11:49:06","video":"","vorDoi":"10.1186/s40001-026-04393-x","vorDoiUrl":"https://doi.org/10.1186/s40001-026-04393-x","workflowStages":[]},"version":"v1","identity":"rs-8087983","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8087983","identity":"rs-8087983","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}