构建可落地的细胞计数MLOps框架，提升分析可靠性与效率
Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
- 设计全流程MLOps框架，整合数据、训练、监控与可解释性
- 实践案例显示模型可靠性提升，人为误差显著减少
- 适合科研与实验室人员部署智能细胞计数系统
机器学习（ML）在神经科学、医学研究、药物开发和环境监测中的细胞计数应用中具有巨大潜力。然而，有效实施这些模型需要稳健的运营框架。本文提出细胞计数机器学习运维（CC-MLOps），一个全面的框架，用于简化ML在细胞计数工作流中的集成。该框架涵盖数据访问与预处理、模型训练、监控、可解释性功能及可持续性考量。通过一个实际用例，我们展示了MLOps原则如何提升模型可靠性、减少人为错误，并实现可扩展的细胞计数解决方案。本研究为寻求部署机器学习驱动细胞计数系统的研究人员和实验室专业人员提供了可操作的指导。
原文摘要 · Abstract (English)
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.
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