In this section
@article{Rabbi:2022:10.3390/mi13020260,
author = {Rabbi, F and Dabbagh, SR and Angin, P and Yetisen, AK and Tasoglu, S},
doi = {10.3390/mi13020260},
journal = {Micromachines (Basel)},
pages = {1--28},
title = {Deep learning-enabled technologies for bioimage analysis.},
url = {http://dx.doi.org/10.3390/mi13020260},
volume = {13},
year = {2022}
}
TY - JOUR
AB - 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.
AU - Rabbi,F
AU - Dabbagh,SR
AU - Angin,P
AU - Yetisen,AK
AU - Tasoglu,S
DO - 10.3390/mi13020260
EP - 28
PY - 2022///
SN - 2072-666X
SP - 1
TI - Deep learning-enabled technologies for bioimage analysis.
T2 - Micromachines (Basel)
UR - http://dx.doi.org/10.3390/mi13020260
UR - https://www.ncbi.nlm.nih.gov/pubmed/35208385
UR - https://www.mdpi.com/2072-666X/13/2/260
UR - http://hdl.handle.net/10044/1/95914
VL - 13
ER -