Feature learning with component selective encoding for histopathology image classification

In this paper, we present a new feature representation method, called the Component Selective Encoding (CSE), for automated histopathology image classification. While the integration of Fisher Vector (FV) encoding with convolutional neural network (CNN) has demonstrated excellent performance in the classification of both general texture and histopathology images, the high dimensionality of FV descriptors could lead to suboptimal performance. Our proposed CSE method provides effective dimensionality reduction that is adaptive to the discriminativeness of individual Gaussian components in the FV descriptors. Evaluation on the publicly available BreaKHis dataset shows that our method outperforms the existing approaches based on deep learning and FV encoding.