WPNet: Wide Pyramid Network for Recognition of HER2 Expression Levels in Breast Cancer Evaluation

Among the research methods for HER2 automatic evaluation in recent years, most of the methods using deep learning framework have both segmentation and classification functions. Although these methods provide pathologists with reference lesions, they increase the dependence on dataset and the computational cost. Therefore, we propose a Wide Pyramid Network (WPNet) based on deep learning to solve this problem. Our designed WPNet is different from other neural network models, which is mainly extended in the width of the network, and uses the wide pyramid structure to extract the features of different scales on the image for training. Since HER2 score is determined according to the degree of cell membrane staining and the proportion of cells with different degrees of staining, the WPNet model capable of multi-scale feature extraction can facilitate the determination of HER2 score. Compared with other models, for HER2 score classification based on a small sample set, the proposed model not only accelerates the convergence speed during training, reduces the calculation cost but also improves the classification effect.