The variety of bioimage data and their quality have dramatically increased over the last decade. In parallel, the number of proposed deep learning (DL) models for their analysis grows by the day. Yet, the adequate reuse of published tools by practitioners without DL expertise still raises many practical questions. In this article, we explore four categories of challenges faced by researchers when using supervised DL models in bioimaging applications. We provide examples in which each challenge arises and review the consequences that inadequate decisions may have. We then outline good practices that can be implemented to address the challenges of each category in a scientifically sound way. We provide pointers to the resources that are already available or in active development to help in this endeavor and advocate for the development of further community-driven standards. While primarily intended as a practical tutorial for life scientists, this article also aims at fostering discussions among method developers around the formulation of guidelines for the adequate deployment of DL, with the ultimate goal of accelerating the adoption of novel DL technologies in the biology community.

中文翻译：

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生物图像分析监督深度学习实用指南：挑战和良好实践


在过去十年中，生物图像数据的种类及其质量显着提高。与此同时，用于分析的深度学习 (DL) 模型的数量与日俱增。然而，没有深度学习专业知识的从业者对已发布工具的充分重用仍然引发了许多实际问题。在本文中，我们探讨了研究人员在生物成像应用中使用监督深度学习模型时面临的四类挑战。我们提供了每个挑战出现的例子，并审查了不适当的决策可能产生的后果。然后，我们概述了可以实施的良好实践，以科学合理的方式应对每个类别的挑战。我们提供已可用或正在积极开发的资源的指示，以帮助实现这一目标，并倡导开发进一步的社区驱动标准。虽然本文的主要目的是作为生命科学家的实用教程，但本文还旨在促进方法开发人员之间围绕制定充分部署深度学习的指南进行讨论，最终目标是加速生物学界采用新颖的深度学习技术。