Improving Breast Cancer Detection using Symmetry Information with Deep Learning
This work addresses improving CAD systems for radiologists in medical imaging, but it is incremental as it builds on existing CNN methods by adding symmetry context.
The paper tackled breast cancer detection in mammograms by integrating symmetry information into a deep learning model, achieving a candidate-level AUC of 0.933 and an exam-level CPM of 0.733, with the latter showing significant improvement over a baseline.
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.