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Convolutional Neural Networks (CNN) have had a huge success in many areas ofcomputer vision and medical image analysis. However, there is still an immensepotential for performance improvement in mammogram breast cancer detectionComputer-Aided Detection (CAD) systems by integrating all the information thatthe radiologist utilizes, such as symmetry and temporal data. In this work, weproposed a patch based multi-input CNN that learns symmetrical difference todetect breast masses. The network was trained on a large-scale dataset of 28294mammogram images. The performance was compared to a baseline architecturewithout symmetry context using Area Under the ROC Curve (AUC) and CompetitionPerformance Metric (CPM). At candidate level, AUC value of 0.933 with 95%confidence interval of [0.920, 0.954] was obtained when symmetry information isincorporated in comparison with baseline architecture which yielded AUC valueof 0.929 with [0.919, 0.947] confidence interval. By incorporating symmetricalinformation, although there was no a significant candidate level performanceagain (p = 0.111), we have found a compelling result at exam level with CPMvalue of 0.733 (p = 0.001). We believe that including temporal data, and addingbenign class to the dataset could improve the detection performance.

Improving Breast Cancer Detection using Symmetry Information with Deep Learning