Improving Breast Cancer Detection using Symmetry Information with Deep Learning

Convolutional Neural Networks (CNN) have had a huge success in many areas of computer vision and medical image analysis. However, there is still an immense potential for performance improvement in mammogram breast cancer detection Computer-Aided Detection (CAD) systems by integrating all the information that the radiologist utilizes, such as symmetry and temporal data. In this work, we proposed a patch based multi-input CNN that learns symmetrical difference to detect breast masses. The network was trained on a large-scale dataset of 28294 mammogram images. The performance was compared to a baseline architecture without symmetry context using Area Under the ROC Curve (AUC) and Competition Performance Metric (CPM). At candidate level, AUC value of 0.933 with 95% confidence interval of [0.920, 0.954] was obtained when symmetry information is incorporated in comparison with baseline architecture which yielded AUC value of 0.929 with [0.919, 0.947] confidence interval. By incorporating symmetrical information, although there was no a significant candidate level performance again (p = 0.111), we have found a compelling result at exam level with CPM value of 0.733 (p = 0.001). We believe that including temporal data, and adding benign class to the dataset could improve the detection performance.