Breast Cancer Histopathological Image Classification Based on Deep Second-order Pooling Network

With the breakthrough performance in a variety of computer vision and medical image analysis problems, convolutional neural networks (CNNs) have been successfully introduced for the classification task of breast cancer histopathological images in recent years. Nevertheless, existing breast cancer histopathological image classification networks mainly utilize the first-order statistic information of deep features to represent histopathological images, failing to characterize the complex global feature distribution of breast cancer histopathological images. To address the problem, this work makes a first attempt to explore global second-order statistics of deep features for the above task. More specifically, we propose a novel deep second-order pooling network (DSoPN) for breast cancer histopathological image classification, in which a robust global covariance pooling module based on matrix power normalization (MPN) is embedded into a simple yet effective CNN architecture. The given DSoPN model can capture richer second-order statistical information of deep convolutional features and produce more informative global representations for breast cancer histopathological images. Experimental results on the public BreakHis dataset illuminate the promising performance of the second-order pooling for breast cancer histopathological image classification. Besides, our DSoPN achieves very competitive performance compared to the state-of-the-art methods.