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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. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignant disease in women, and early screening of breast cancer is crucial for improving the survival rate. Mammography is one of the most popular imaging methods for breast cancer screening with the characteristics of practicality, effectiveness, and low cost. However, the classification of mammograms suffers from large image sizes, indistinct image characteristics of lesions, small proportion of abnormalities, and class imbalance. To address these difficulties, the multi-view input and weighted multi-instance learning (MIL) methods are proposed. A novel model called the weighted MIL DenseNet with multi-view input (WMDNet) is presented that integrates the two methods above. The multi-view inputs method is used to enhance the abnormalities of mammograms and obtain more potential features from mammograms with different views, simultaneously. The weighted MIL is designed to extract the most suspicious lesions from mammograms to resolve the problems of small proportion of abnormalities and class imbalance. To verify the effectiveness of the proposed methods, three binary classification models are evaluated on two public datasets, the INbreast and MIAS datasets. The experimental results demonstrate that the proposed methods can achieve preferably results compared with several other state-of-the-art approaches, especially the proposed WMDNet model gains the best classification results on both datasets. [ABSTRACT FROM AUTHOR]