Classification of breast cancer pathological images combining fine-grained region location
Shihao Ma, Bei Yang, Guilan Tian, Xiaoyu Li · 2023
Classification algorithm based on deep learning is the main technology for computer-aided intelligent diagnosis of breast pathological images. The existing deep learning algorithms rarely pay attention to multi-scale information in breast cancer pathological images, and can not extract key regions from pathological images for auxiliary diagnosis. To address this issue, this paper proposes a novel method for classifying breast cancer pathological images which incorporates a fine-grained region location mechanism. This method is realized by a dual-branch architecture with global network and local network. Global and local features are extracted from the whole image and local key image, respectively. The final classification results can be obtained by integrating both the global and local network analysis. In order to locate the most predictive area in the whole image, this paper designs utilizes a fine-grained region localization mechanism and combines the above two branch networks. Extensive experiments on the BreakHis data set are conducted to verify the effectiveness of the proposed algorithm. The empirical results show that this method improves the classification accuracy by comparing the performance with that of several typical convolutional neural network and state-of-the-art algorithms.