  Some recent studies have described deep convolutional neural networks to
diagnose breast cancer in mammograms with similar or even superior performance
to that of human experts. One of the best techniques does two transfer
learnings: the first uses a model trained on natural images to create a "patch
classifier" that categorizes small subimages; the second uses the patch
classifier to scan the whole mammogram and create the "single-view whole-image
classifier". We propose to make a third transfer learning to obtain a "two-view
classifier" to use the two mammographic views: bilateral craniocaudal and
mediolateral oblique. We use EfficientNet as the basis of our model. We
"end-to-end" train the entire system using CBIS-DDSM dataset. To ensure
statistical robustness, we test our system twice using: (a) 5-fold cross
validation; and (b) the original training/test division of the dataset. Our
technique reached an AUC of 0.9344 using 5-fold cross validation (accuracy,
sensitivity and specificity are 85.13% at the equal error rate point of ROC).
Using the original dataset division, our technique achieved an AUC of 0.8483,
as far as we know the highest reported AUC for this problem, although the
subtle differences in the testing conditions of each work do not allow for an
accurate comparison. The inference code and model are available at
https://github.com/dpetrini/two-views-classifier
