Deep learning-enabled technologies for bioimage analysis

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.
Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2232 International Fellowship for Outstandig Researchers Award; European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Alexander von Humboldt Research Fellowship for Experienced Researchers; Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership