Deep Learning-Based Classification of Protein Subcellular Localization from Immunohistochemistry Images

Abstract

Due to the recent breakthrough of bioimaging, automated classification of protein subcellular localization based on immunohistochemistry (IHC) images has become an important topic of proteomics research. Inspired by the impressive performance of deep learning in various image classifications, we trained a deep neural network model to classify protein images of eight subcellular localizations, which is able to achieve higher classification accuracies than using traditional models of support vector machine. Intermediate outputs of the neural network were visualized to show that our model can capture subtle texture features from IHC images and lead to better subcellular location classification results. In addition, our results show that data rebalance can significantly improve the classification performance in this multi-class deep classifier