A Multi-Task Learning Model for Evaluating Non-Tumor Gastric Diseases Indicators in Whole Slide Images

A Multi-Task Learning Model for Evaluating Non-Tumor Gastric Diseases Indicators in Whole Slide Images | 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 A Multi-Task Learning Model for Evaluating Non-Tumor Gastric Diseases Indicators in Whole Slide Images Mingxi Fu, Liming Liu, Fanglei Fu, Jingli Ouyang, Tian Guan, Yonghong He, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6695941/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Inflammation, acute activity, intestinal metaplasia, and atrophy are key indicators in gastric biopsy evaluations, with their grading being crucial for assessing gastric cancer progression. However, diagnostic subjectivity among pathologists and the complex interrelationships between these indicators present significant challenges. Additionally, the high resolution of whole slide images (WSIs) complicates large-scale annotation efforts. To address these issues, we propose a multi-task learning model utilizing self-supervised pre-trained weights from extensive pathological datasets. The model integrates four indicators—severity of inflammation, atrophy, acute activity, and intestinal metaplasia—training on WSIs to predict these indicators while accounting for their interrelationships. Our results show that multi-task learning outperforms single-task models, achieving higher accuracy across all indicators. This model can thus serve as an auxiliary tool for evaluating non-tumor gastric diseases and supporting diagnosis. Biological sciences/Computational biology and bioinformatics/Image processing Health sciences/Diseases Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Supplementary Files appendix.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 30 May, 2025 Editor invited by journal 29 May, 2025 Submission checks completed at journal 21 May, 2025 First submitted to journal 21 May, 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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However, diagnostic subjectivity among pathologists and the complex interrelationships between these indicators present significant challenges. Additionally, the high resolution of whole slide images (WSIs) complicates large-scale annotation efforts. To address these issues, we propose a multi-task learning model utilizing self-supervised pre-trained weights from extensive pathological datasets. The model integrates four indicators—severity of inflammation, atrophy, acute activity, and intestinal metaplasia—training on WSIs to predict these indicators while accounting for their interrelationships. Our results show that multi-task learning outperforms single-task models, achieving higher accuracy across all indicators. 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