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

2025/04/28 by Matteo Testi, Luca Clissa, Testi, Matteo +13
Biochemistry, Genetics and Molecular Biology · Engineering · #Applied Physics (physics.app-ph) #Cell Image Analysis Techniques #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Microfluidic and Bio-sensing Technologies #Single-cell and spatial transcriptomics #Software Engineering (cs.SE)

paper · pdf · doi:10.48550/arxiv.2504.20126

openalex publication_date 2025/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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 (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.

Citations

• Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy
• Edge Impulse: An MLOps Platform for Tiny Machine Learning
• On Continuous Integration / Continuous Delivery for Automated Deployment of Machine Learning Models using MLOps
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• Explanation in Artificial Intelligence: Insights from the Social Sciences
• Explanation in artificial intelligence: Insights from the social sciences
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
• A Systematic Literature Review on Applying CRISP-DM Process Model

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