RT Journal Article
SR Electronic
T1 Automating Morphological Profiling with Generic Deep Convolutional Networks
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 085118
DO 10.1101/085118
A1 Nick Pawlowski
A1 Juan C Caicedo
A1 Shantanu Singh
A1 Anne E Carpenter
A1 Amos Storkey
YR 2016
UL http://biorxiv.org/content/early/2016/11/02/085118.abstract
AB Morphological profiling aims to create signatures of genes, chemicals and diseases from microscopy images. Current approaches use classical computer vision-based segmentation and feature extraction. Deep learning models achieve state-of-the-art performance in many computer vision tasks such as classification and segmentation. We propose to transfer activation features of generic deep convolutional networks to extract features for morphological profiling. Our approach surpasses currently used methods in terms of accuracy and processing speed. Furthermore, it enables fully automated processing of microscopy images without need for single cell identification.