  Breast cancer classification remains a challenging task due to inter-class
ambiguity and intra-class variability. Existing deep learning-based methods try
to confront this challenge by utilizing complex nonlinear projections. However,
these methods typically extract global features from entire images, neglecting
the fact that the subtle detail information can be crucial in extracting
discriminative features. In this study, we propose a novel method named
Attention Model Enhanced Network (AMEN), which is formulated in a multi-branch
fashion with pixel-wised attention model and classification submodular.
Specifically, the feature learning part in AMEN can generate pixel-wised
attention map, while the classification submodular are utilized to classify the
samples. To focus more on subtle detail information, the sample image is
enhanced by the pixel-wised attention map generated from former branch.
Furthermore, boosting strategy are adopted to fuse classification results from
different branches for better performance. Experiments conducted on three
benchmark datasets demonstrate the superiority of the proposed method under
various scenarios.
