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 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Review
  • Volume: 13 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.3390/mi13020260
  • Journal Name: Micromachines
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Compendex, INSPEC, Metadex, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: deep learning, machine learning, bioimage quantification, cell morphology classification, cancer diagnosis, HIGH-THROUGHPUT, NEURAL-NETWORK, SINGLE-CELL, KIDNEY-DISEASE, EYE DISEASES, LABEL-FREE, LOW-COST, BIG DATA, CANCER, MODEL
  • Middle East Technical University Affiliated: Yes

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.