MRC-Laboratory for Molecular Cell Biology, University College London, London, UK.
The Francis Crick Institute, London, UK.
Nat Methods. 2021 Oct;18(10):1136-1144. doi: 10.1038/s41592-021-01284-3.
Deep learning algorithms are powerful tools to analyse, restore and transform bioimaging data, increasingly used in life sciences research. These approaches now outperform most other algorithms for a broad range of image analysis tasks. In particular, one of the promises of deep learning is the possibility to provide parameter-free, one-click data analysis achieving expert-level performances in a fraction of the time previously required. However, as with most new and upcoming technologies, the potential for inappropriate use is raising concerns among the biomedical research community. This perspective aims to provide a short overview of key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. These comments are based on our own experience gained while optimising various deep learning tools for bioimage analysis and discussions with colleagues from both the developer and user community. In particular, we focus on describing how results obtained using deep learning can be validated and discuss what should, in our views, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis would need to be reported in publications to describe the use of such tools to guarantee that the work can be reproduced. We hope this perspective will foster further discussion between developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure that this transformative technology is used appropriately.
深度学习算法是分析、还原和转换生物成像数据的强大工具，越来越多地应用于生命科学研究。这些方法现在在广泛的图像分析任务中优于大多数其他算法。特别是，深度学习的一个承诺是有可能提供无参数、一键式数据分析，以以前所需时间的一小部分达到专家级别的性能。然而，与大多数新出现的技术一样，其不适当使用的可能性引起了生物医学研究界的关注。本文旨在提供一个简短的概述，介绍我们认为研究人员在使用深度学习进行显微镜研究时需要考虑的关键概念。这些评论是基于我们在优化各种用于生物图像分析的深度学习工具时获得的经验以及与开发人员和用户社区的同事的讨论。特别是，我们专注于描述如何验证使用深度学习获得的结果，并讨论在选择合适的工具时应考虑哪些因素。我们还建议在出版物中报告深度学习分析的哪些方面，以描述此类工具的使用，以确保工作可以重现。我们希望这一观点将促进开发人员、图像分析专家、用户和期刊编辑之间的进一步讨论，以定义适当的准则，并确保这项变革性技术得到适当的应用。
