PT  - JOURNAL ARTICLE
AU  - Nick Pawlowski
AU  - Juan C Caicedo
AU  - Shantanu Singh
AU  - Anne E Carpenter
AU  - Amos Storkey
TI  - Automating Morphological Profiling with Generic Deep Convolutional Networks
AID  - 10.1101/085118
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 085118
4099  - http://biorxiv.org/content/early/2016/11/02/085118.short
4100  - http://biorxiv.org/content/early/2016/11/02/085118.full
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.