Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913812049821696 |
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| author | Testi, Matteo Clissa, Luca Ballabio, Matteo Ricciardi, Salvatore Baldo, Federico Frontoni, Emanuele Moccia, Sara Vessio, Gennario |
| author_facet | Testi, Matteo Clissa, Luca Ballabio, Matteo Ricciardi, Salvatore Baldo, Federico Frontoni, Emanuele Moccia, Sara Vessio, Gennario |
| contents | 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 (CC-MLOps), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis Testi, Matteo Clissa, Luca Ballabio, Matteo Ricciardi, Salvatore Baldo, Federico Frontoni, Emanuele Moccia, Sara Vessio, Gennario Software Engineering Machine Learning Applied Physics 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 (CC-MLOps), 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. |
| title | Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis |
| topic | Software Engineering Machine Learning Applied Physics |
| url | https://arxiv.org/abs/2504.20126 |