Transfer Learning with Deep Convolutional Neural Networks for Classifying Cellular Morphological Changes.

ABSTRACT: The quantification and identification of cellular phenotypes from high-content microscopy images has proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which requires several nontrivial and independent analysis steps. Recently, convolutional neural networks have emerged as a compelling alternative, offering good predictive performance and the possibility to replace traditional workflows with a single network architecture. In this study, we applied the pretrained deep convolutional neural networks ResNet50, InceptionV3, and InceptionResnetV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profi

SUBMITTER: Kensert A 

PROVIDER: S-EPMC6484664 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature