Breast cancer histopathology image classification through assembling multiple compact CNNs

Breast cancer causes hundreds of thousands of deaths each year worldwide. The early stage diagnosis and treatment can significantly reduce the mortality rate. However, the traditional manual diagnosis needs intense workload, and diagnostic errors are prone to happen with the prolonged work of pathologists. Automatic histopathology image recognition plays a key role in speeding up diagnosis and improving the quality of diagnosis.

In this work, we propose a breast cancer histopathology image classification by assembling multiple compact Convolutional Neural Networks (CNNs). First, a hybrid CNN architecture is designed, which contains a global model branch and a local model branch. By local voting and two-branch information merging, our hybrid model obtains stronger representation ability. Second, by embedding the proposed Squeeze-Excitation-Pruning (SEP) block into our hybrid model, the channel importance can be learned and the redundant channels are thus removed. The proposed channel pruning scheme can decrease the risk of overfitting and produce higher accuracy with the same model size. At last, with different data partition and composition, we build multiple models and assemble them together to further enhance the model generalization ability.

Experimental results show that in public BreaKHis dataset, our proposed hybrid model achieves comparable performance with the state-of-the-art. By adopting the multi-model assembling scheme, our method outperforms the state-of-the-art in both patient level and image level accuracy for BACH dataset.

We propose a novel compact breast cancer histopathology image classification scheme by assembling multiple compact hybrid CNNs. The proposed scheme achieves promising results for the breast cancer image classification task. Our method can be used in breast cancer auxiliary diagnostic scenario, and it can reduce the workload of pathologists as well as improve the quality of diagnosis.