Breast cancer histopathology image classification based on dual-stream high-order network

Abstract

The early diagnosis of breast cancer using pathological images is of the vital importance. Recently, breast cancer histopathology image classification methods based on convolution neural network (CNN) are constantly innovating with the development of computer-aided diagnosis technology. To obtain pathological tissue features with more discriminant presentation capability for classification, this work proposes a novel dual-stream high-order breast cancer pathological image classification network named DsHoNet. To be precise, a shallow network composed of six convolution layers is built as the backbone of the dual-stream network firstly, in which one stream utilizes batch normalization (BN) layer to retain the original feature information with clearer feature distribution, while another stream introduces the Ghost module to extract richer supplementary features by utilizing a series of linear transformations. Then, outputs of the two streams are further enhanced via a covariance pooling layer to achieve more powerful deep high-order statistic features for classification. Extensive evaluation experiments carried out on the public BreakHis dataset demonstrate that the optimal recognition rates of DsHoNet are 99.01% and 99.25% respectively at the image-level and patient-level, performing favorably against its counterparts.