Deep Learning-Enabled Technologies for Bioimage Analysis

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micromachines-13-00260-v2.pdf

Date

2022-02-01

Author

Rabbi, Fazle
Dabbagh, Sajjad Rahmani
Angın, Pelin
Yetisen, Ali Kemal
Tasoglu, Savas

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

Subject Keywords

Bioimage quantification, Cancer diagnosis, Cell morphology classifica-tion, Deep learning, Machine learning

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124353297&origin=inward
https://hdl.handle.net/11511/97034

Journal

Micromachines

DOI

https://doi.org/10.3390/mi13020260

Collections

Department of Computer Engineering, Article

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Citation Formats

F. Rabbi, S. R. Dabbagh, P. Angın, A. K. Yetisen, and S. Tasoglu, “Deep Learning-Enabled Technologies for Bioimage Analysis,” Micromachines, vol. 13, no. 2, pp. 0–0, 2022, Accessed: 00, 2022. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85124353297&origin=inward.