A deep learning-based classification algorithm was proposed aiming at the problem of automatic classification algorithms focusing on breast cancer histopathological images. Channel squeeze-and-excitation (SE) model is an attention model applied to the feature channels. Useless features can be suppressed with learned channel weights so as to recalibrate the feature channels for better classification accuracy. A multi-scale channel SE model was proposed, and a convolutional neural network named msSE-ResNet was designed in order to make the result of channel recalibration more accurate. Multi-scale features were obtained by max-pooling layers and served as inputs to subsequent channel SE models, and the result of channel recalibration was improved by merging channel weights learned under different feature scales. Experiments were conducted on public dataset BreaKHis. Results show that the network can reach an accuracy of 88.87% on the task of classifying benign/malignant breast histopathological images, and can remain good robustness to histopathological images acquired under different magnifications.
Keywords：breast cancer histopathological image classification
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deep learning
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residual network
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multi-scale feature
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channel squeeze-and-excitation model
MING Tao, WANG Dan, GUO Ji-chang, LI Qiang. Breast cancer histopathological image classification using multi-scale channel squeeze-and-excitation model. Journal of Zhejiang University(Engineering Science)[J], 2020, 54(7): 1289-1297 doi:10.3785/j.issn.1008-973X.2020.07.006
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