Deep learning a boon for biophotonics?

Autor: Pradhan P; 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., Guo S; 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., Ryabchykov O; 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., Popp J; 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., Bocklitz TW; 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.
Jazyk: angličtina
Zdroj: Journal of biophotonics [J Biophotonics] 2020 Jun; Vol. 13 (6), pp. e201960186. Date of Electronic Publication: 2020 Mar 30.
DOI: 10.1002/jbio.201960186
Abstrakt: 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.
(© 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)
Databáze: MEDLINE
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