A novel deep learning model CSAM-ResNet based on attention mechanism and residual learning for breast histopathology image classification

Histopathological image analysis is the gold standard for breast cancer diagnosis. In recent years, the pathological image recognition technology based on deep learning has attracted great attention. However, a current issue with existing methods is the lack of effective extraction of overall contextual information and key details from the images. To this end, we proposed an efficient pathological image recognition model CSAM-ResNet based on channel attention mechanism, spatial attention mechanism, residual learning principle and multi-scale fusion method. The spatial and channel attention mechanism (CSAM) combines the advantages of capturing long-distance feature dependencies in Transformer and local feature extraction in CNN. Therefore, CSAM-ResNet can extract the global context information in the image more comprehensively, while strengthening the sensitivity to the key information. To evaluate the effectiveness of the proposed method in this paper, we experimentaly compared the CSAM-ResNet with SE-ResNet and SwinTransformer on the authoritative breast cancer pathological image classification datasets PatchCamelyon (Pcam) and BreakHis. It is shown that CBAM-ResNet improves classification accuracy by 0.3% and 0.4% in the Pcam and BreakHis datasets, respectively. The code is available on site: https://github.com/luckyrz0/CSAM-ResNet.