关键词: 乳腺X线摄影, 注意力机制, 卷积神经网络, 循环神经网络, 预训练

Abstract:

The worsening environment has led to an increase in the incidence of cancer. In 2018, the incidence of breast cancer has ranked first among all cancers in the world. Considering that mammography is affordable and easy to operate, it is currently regarded as the best screening method for breast cancer and the most effective method for early detection of breast cancer. In view of the fact that mammography is not easy to distinguish and its features are not obvious, this paper proposes an attention memory network based on RNN + CNN to classify it. The attention memory network includes the attention memory module and the convolution residual module. In the attention memory module, the attention module is used to extract features of mammography, and the memory module adds attention weight to RNN network to simulate people’s emphasis on key information. Convolution residual module uses CNN to classify images. The contributions of this paper are as follows, the attention memory network is proposed for mammography image classification, the designed network introduces attention weights on the RNN+CNN structure, and extracts key information of images to enhance the feature description. The experimental results on the mammography INbreast dataset show that the runtime of attention memory network can be 50% less than the pre-trained Inception v2, ResNet50, and VGG16, and can achieve higher classification accuracy than others.

Key words: mammography, attention mechanism, Convolutional Neural Network（CNN）, Recurrent Neural Network（RNN）, pre-training