at Google ScholarTitle data
Pradhan, Pranita ; Guo, Shuxia ; Ryabchykov, Oleg ; Popp, Jürgen ; Bocklitz, Thomas:
Deep learning a boon for Biophotonics?
In: Journal of Biophotonics.
Vol. 13
(2020)
Issue 6
.
- e201960186.
ISSN 1864-0648
DOI: https://doi.org/10.1002/jbio.201960186
Abstract in another language
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 analyse biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudo-staining 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.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Keywords: | biophotonics; spectroscopy; deep learning; artificial neural networks |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie > Lehrstuhl Künstliche Intelligenz in der Mikroskopie und Spektroskopie - Univ.-Prof. Dr. Thomas Wilhelm Bocklitz |
| Result of work at the UBT: | No |
| DDC Subjects: | 500 Science > 530 Physics |
| Date Deposited: | 15 May 2023 12:46 |
| Last Modified: | 15 May 2023 12:46 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76355 |