Deep Learning-Enabled Technologies for Bioimage Analysis


Rabbi F., Dabbagh S. R. , ANGIN P. , Yetisen A. K. , Tasoglu S.

Micromachines, vol.13, no.2, 2022 (Journal Indexed in SCI Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 13 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.3390/mi13020260
  • Title of Journal : Micromachines
  • Keywords: Bioimage quantification, Cancer diagnosis, Cell morphology classifica-tion, Deep learning, Machine learning

Abstract

© 2022 by the authors. Licensee MDPI, Basel, Switzerland.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.