Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich-Schiller-University, Jena, Germany.
Leibniz Institute of Photonic Technology (Leibniz-IPHT), Member of Leibniz Research Alliance 'Health Technologies', Jena, Germany.
J Biophotonics. 2020 Jun;13(6):e201960186. doi: 10.1002/jbio.201960186. Epub 2020 Mar 30.
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.
这篇综述涵盖了近年来在生物光子学领域使用深度学习的原创文章。深度学习是机器学习的一个子集，主要基于人工神经网络几何结构，已被应用于许多生物光子学任务，并取得了最新的成果。因此，生物光子学领域的深度学习正在迅速发展，并将在未来几年内用于获得实时生物光子决策系统和一般分析生物光子数据。在本贡献中，我们讨论了深度学习在生物光子学领域的可能性，包括图像分类、分割、配准、伪染色和分辨率增强。此外，我们还讨论了深度学习在光谱数据中的潜在应用，包括光谱数据预处理和光谱分类。最后，我们讨论了使用深度学习处理生物光子数据的潜在应用和挑战。
