显微镜中的机器学习--见解、机遇与挑战。

IF 3.3 3区 生物学 Q3 CELL BIOLOGY
Journal of cell science Pub Date : 2024-10-15 Epub Date: 2024-10-28 DOI:10.1242/jcs.262095
Inês Cunha, Emma Latron, Sebastian Bauer, Daniel Sage, Juliette Griffié
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引用次数: 0

摘要

机器学习(ML)正在改变图像处理和分析领域,从繁重任务的自动化到视觉模式的开放式探索。这对以图像为驱动力的生命科学研究,尤其是显微镜研究具有重大影响。在本综述中,我们从用户的角度出发,重点探讨了将基于 ML 的管道应用于显微镜数据集的相关机遇和挑战。我们研究了不同数据特征(数量、可转移性和内容)的重要性,以及这如何决定使用哪种 ML 模型及其输出。在细胞生物学问题和应用的背景下,我们进一步讨论了 ML 的实用范围,即数据整理、探索、预测和解释,以及它们在显微镜下的含义和转化。最后,我们探讨了与显微镜中的人工智能相关的挑战、常见人工制品和风险。基于对其他领域的深入了解,我们提出了如何在显微镜中减少这些隐患。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning in microscopy - insights, opportunities and challenges.

Machine learning (ML) is transforming the field of image processing and analysis, from automation of laborious tasks to open-ended exploration of visual patterns. This has striking implications for image-driven life science research, particularly microscopy. In this Review, we focus on the opportunities and challenges associated with applying ML-based pipelines for microscopy datasets from a user point of view. We investigate the significance of different data characteristics - quantity, transferability and content - and how this determines which ML model(s) to use, as well as their output(s). Within the context of cell biological questions and applications, we further discuss ML utility range, namely data curation, exploration, prediction and explanation, and what they entail and translate to in the context of microscopy. Finally, we explore the challenges, common artefacts and risks associated with ML in microscopy. Building on insights from other fields, we propose how these pitfalls might be mitigated for in microscopy.

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来源期刊
Journal of cell science
Journal of cell science 生物-细胞生物学
CiteScore
7.30
自引率
2.50%
发文量
393
审稿时长
1.4 months
期刊介绍: Journal of Cell Science publishes cutting-edge science, encompassing all aspects of cell biology.
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