Cell Phenotype Classification Using Deep Residual Network and Its Variants

Deep residual network (ResNet) is currently the basis of many popular state-of-the-art convolutional neural network models for image recognition, and its recent variants include wide residual network (WRN), aggregated deep residual network (ResNeXt) and deep pyramidal residual network (PyramidNet). Here, we demonstrate the potential application of deep residual network and its variants in high-content screening (i.e. cell phenotype classification) that can overcome issues associated with analyzing high-content screening data, such as exhaustive preprocessing and inefficient learning. Cell phenotype classification is an image-based method that can be used for drug high-content screening, in which complex cell states associated with chemical compound treatment can be characterized. Previous work on cell phenotype classification typically requires a routine yet cumbersome step of single cell segmentation before the classification task. In this paper, we present a segmentation-free method for image-based cell phenotype classification using deep ResNet and its variants. The cell images are samples treated with annotated compounds that can be mainly grouped into three clusters, giving three classes to be classified. Instead of single-cell phenotype classification, we use the raw images without segmentation for our training and evaluation directly. Compared to previous reference work, we significantly simplify the data preprocessing step and accelerate the training while still achieving high accuracy. Our trained models achieve a 98.2% accuracy rate on the three classes classification problem (three compound clusters only), and a 93.8% accuracy rate on the four classes classification problem (three compound clusters plus the mock class) based on five-fold cross-validation.