Rabbi Fazle, Dabbagh Sajjad Rahmani, Angin Pelin, Yetisen Ali Kemal, Tasoglu Savas
Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey.
Koç University Arçelik Research Center for Creative Industries (KUAR), Koç University, Sariyer, Istanbul 34450, Turkey.
Micromachines (Basel). 2022 Feb 6;13(2):260. doi: 10.3390/mi13020260.
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.
深度学习（DL）是机器学习（ML）的一个子领域，最近已证明其在显著改善生物医学和临床应用中的量化和分类工作流程方面的潜力。在从深度学习中受益匪浅的最终应用中，细胞形态量化是先驱之一。在这里，我们首先简要解释深度学习的基本概念，然后回顾一些在胚胎学、即时排卵检测、作为胎儿心脏妊娠预测工具、通过癌症组织学图像分类进行癌症诊断、常染色体多囊肾病和慢性肾病等领域中新兴的基于深度学习的细胞形态量化应用。
