DeephESC: An Automated System for Generating and Classification of Human Embryonic Stem Cells

Human Embryonic Stem Cells (hESC's) are promising for the treatment of many diseases such as cancer, Parkinsons, Huntingtons, diabetes mellitus etc. and for toxicological testing. Automated detection and classification of human embryonic stem cell (hESC) videos is of great interest among biologists for quantified analysis of various states of hESC in experimental work. To date, the biologists who study hESC's have to analyze stem cell videos manually. In this paper we introduce a hierarchical classification system consisting of Convolutional Neural Networks (CNN) and Triplet CNN's to classify hESC images into six different classes. We also design an ensemble of Generative Adversarial Networks (GAN) for generating synthetic images of hESC's. We validate the quality of the generated hESC images by training all of our CNN's exclusively on the synthetic images generated by the GAN's and evaluating them on the original hESC images. Experimental results shows that we classify the original hESC images, with an accuracy of 85.67% using the CNN alone, 91.38% accuracy using the CNN and Triplet CNN and 94.11% accuracy by fusing the outputs of the CNN and Triplet CNN's, out performing existing state-of-the-art approaches.