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.

Funding

Our work in this area is supported by the Knut and Alice Wallenberg foundation (KAW 2020.0239) and a Data-Driven Life Science (DDLS) fellowship (I.C., E.L.S.B. and J.G.).

Special Issue

This article is part of the Special Issue ‘Imaging Cell Architecture and Dynamics’, guest edited by Lucy Collinson and Guillaume Jacquemet. See related articles at https://journals.biologists.com/jcs/issue/137/20.

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