Deep Learning-Enabled Technologies for Bioimage Analysis

Autor: Fazle Rabbi, Sajjad Rahmani Dabbagh, Pelin Angin, Ali Kemal Yetisen, Savas Tasoglu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Micromachines, Vol 13, Iss 2, p 260 (2022)
Druh dokumentu: article
ISSN: 2072-666X
DOI: 10.3390/mi13020260
Popis: Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical 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.
Databáze: Directory of Open Access Journals