MEF-YOLO A deep lightweight model for solving the fine-grained of organoid

MEF-YOLO A deep lightweight model for solving the fine-grained of organoid | 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 MEF-YOLO A deep lightweight model for solving the fine-grained of organoid Hanwen Zhang, Qin Gao, Wentao Zheng, Xuan Huang, Hui Zhao, Gangyin Luo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6950727/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 Organoids are miniature simplified in vitro model systems that simulate organ structure and function. Despite their utility, challenges remain in addressing organoid assembly and related data analysis. In the context of integrating organoid technology with deep learning, this study proposes a lightweight deep algorithm to efficiently handle high-throughput, multimodal, and fine-grained organoid images, with a primary focus on intestinal organoid images.The study employs the lightweight YOLOv10n as the baseline model for organoid image analysis, introduces a novel organoid image information fusion architecture, and completes the theoretical and engineering design of a specialized algorithm framework for organoid images. Through rigorous experiments—including model comparison analyses, organoid receptive field visualization, and organoid feature attention distribution studies—and performance comparisons with classical models, this work demonstrates how deep learning overcomes the fine-grained analysis challenge in organoid images. Notably, the approach reduces model complexity while enhancing computational efficiency and inference speed for organoid images. This study achieves state-of-the-art organoid recognition performance with minimized computational overhead, offering a new pattern recognition methodology for organoid morphological evaluation. In conclusion, this research presents an innovative technical tool that integrates superior computational performance with real-time multi-dimensional scientific prediction of organoid morphology. Biological sciences/Computational biology and bioinformatics Biological sciences/Computational biology and bioinformatics/Classification and taxonomy Biological sciences/Computational biology and bioinformatics/Image processing Organoids Fine-grained Deep Learning Lightweight 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-6950727","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490360732,"identity":"3b4d2e4a-5511-45a2-8c6b-e092e29b68b7","order_by":0,"name":"Hanwen 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