A Multi-instance Networks with Multiple Views for Classification of Mammograms

Abstract 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.