This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state‐of‐the‐art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real‐time biophotonic decision‐making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data.

中文翻译：

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深度学习生物光子学的福音？

这篇综述涵盖了最近几年在生物光子领域使用深度学习的原创文章。近年来，深度学习是机器学习的一个子集，主要基于人工神经网络的几何形状，已应用于许多生物光子任务，并取得了最先进的性能。因此，生物光子领域的深度学习正在迅速发展，并将在未来几年中用于获得实时生物光子决策系统并总体上分析生物光子数据。在这项贡献中，我们讨论了在生物光子领域进行深度学习的可能性，包括图像分类，分割，配准，伪染色和分辨率增强。另外，我们讨论了深度学习在光谱数据（包括光谱数据预处理和光谱分类）中的潜在用途。我们通过解决将深度学习用于生物光子数据的潜在应用和挑战来结束本综述。