Breast cancer is the most commonly diagnosed cancer among women. Convolutional neural networks (CNN)-based mammogram classification plays a vital role in early breast cancer detection. However, it pays too much attention to the lesions of mammograms and ignores the global characteristics of the breast. In the process of diagnosis, doctors not only pay attention to the features of local lesions but also combine with the comparison to the global characteristics of breasts. Mammogram images have a visible characteristic, which is that the original image is large, while the lesions are relatively small. It means that the lesions are easy to overlook. This paper proposes an automated multi-scale end-to-end deep neural networks model for mammogram classification, that only requires mammogram images and class labels (without ROI annotations). The proposed model generated three scales of feature maps that make the classifier combine global information with the local lesions for classification. Moreover, the images processed by our method contain fewer non-breast pixels and retain the small lesions information as much as possible, which is helpful for the model to focus on the small lesions. The performance of our method is verified on the INbreast dataset. Compared to other state-of-the-art mammogram classification algorithms, our model performs the best. Moreover, the multi-scale method is applied to the networks with fewer parameters that can achieve comparable performance, while saving 60% of the computing resources. It shows that the multi-scale method can work for both performance and computational efficiency.

乳腺癌是女性中最常被诊断出的癌症。基于卷积神经网络（CNN）的乳房X线照片分类在早期乳腺癌检测中起着至关重要的作用。但是，它对乳房X线照片的损伤过于关注，并且忽略了乳房的整体特征。在诊断过程中，医生不仅要注意局部病变的特征，还要结合对乳房整体特征的比较。乳房X光照片具有可见特征，即原始图像较大，而病变相对较小。这意味着病变很容易被忽视。本文提出了一种用于乳房X线照片分类的自动化多尺度端到端深度神经网络模型，该模型仅需要乳房X线照片图像和类别标签（无ROI注释）。提出的模型生成了三个比例尺的特征图，这些特征图使分类器将全局信息与局部病变结合起来进行分类。此外，通过我们的方法处理的图像包含较少的非乳房像素，并尽可能保留小病变信息，这有助于模型将注意力集中在小病变上。我们的方法的性能在INbreast数据集上得到了验证。与其他最新的乳房X线照片分类算法相比，我们的模型表现最佳。此外，将多尺度方法应用于具有较少参数的网络，这些参数可以实现相当的性能，同时节省60％的计算资源。结果表明，多尺度方法既可以提高性能，又可以提高计算效率。