Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis

Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis | 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 Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis matteo testi, Luca Clissa, Matteo Ballabio, Salvatore Ricciardi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6977659/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract Machine Learning (ML) models offer significant potential for advancing cell counting applications in neuroscience, medical research, pharmaceutical development, and environmental monitoring. However, implementing these models effectively requires robust operational frameworks. This paper introduces Cell Counting Machine Learning Operations (CCMLOps), a comprehensive framework that streamlines the integration of ML in cell counting workflows. CC-MLOps encompasses data access and preprocessing, model training, monitoring, explainability features, and sustainability considerations. Through a practical use case, we demonstrate how MLOps principles can enhance model reliability, reduce human error, and enable scalable Cell Counting solutions. This work provides actionable guidance for researchers and laboratory professionals seeking to implement machine learning (ML)- powered cell counting systems. MLOps Machine Learning Cell Counting Bioinformatics Deep Learning Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revision 10 Apr, 2026 Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 11 Dec, 2025 Editor invited by journal 23 Nov, 2025 First submitted to journal 31 Aug, 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. 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-6977659","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558802137,"identity":"b6292685-baaa-49e3-9692-1103e6d6ae81","order_by":0,"name":"matteo 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