Transfer learning with deep convolutional neural network for classifying cellular morphological changes
Kensert A, Harrison PJ, Spjuth O.
Transfer learning with deep convolutional neural network for classifying cellular morphological changes.
Accepted in SLAS Discovery. (2018). DOI: 10.1101/345728
Quantification and identification of cellular phenotypes from high content microscopy images have proven to be very useful for understanding biological activity in response to different drug treatments. The common approach has been to use classical image analysis to quantify changes in cell morphology, which requires several non-trivial 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 pre-trained deep convolutional neural networks ResNet50, InceptionV3 and InceptionV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profiling datasets from the Broad Bioimage Benchmark Collection. These networks were pre-trained on ImageNet which lead to much quicker model training. We obtain higher predictive accuracy than previously reported, between 94 and 97%. The ability to quickly and accurately distinguish between different cell morphologies from a scarce amount of labelled data illustrates the combined benefit of transfer learning and deep convolutional neural networks for interrogating cell-based images.