Deep Understanding of Breast Density Classification.

We have developed a deep learning architecture, DualViewNet, for mammogram density classification as well as a novel metric for quantifying network preference of mediolateral oblique (MLO) versus craniocaudal (CC) views in density classification. Also, we have provided thorough analysis and visualization to better understand the behavior of deep neural networks in density classification. Our proposed architecture, DualViewNet, simultaneously examines and classifies both MLO and CC views corresponding to the same breast, and shows best performance with a macro average AUC of 0.8970 and macro average 95% confidence interval of 0.8239-0.9450 obtained via bootstrapping 1000 test sets. By leveraging DualViewNet we provide a novel algorithm and quantitative comparison of MLO versus CC views for classification and find that MLO provides stronger influence in 1,187 out of 1,323 breasts.