  Breast cancer is the most common cancer in women, and hundreds of thousands
of unnecessary biopsies are done around the world at a tremendous cost. It is
crucial to reduce the rate of biopsies that turn out to be benign tissue. In
this study, we build deep neural networks (DNNs) to classify biopsied lesions
as being either malignant or benign, with the goal of using these networks as
second readers serving radiologists to further reduce the number of false
positive findings. We enhance the performance of DNNs that are trained to learn
from small image patches by integrating global context provided in the form of
saliency maps learned from the entire image into their reasoning, similar to
how radiologists consider global context when evaluating areas of interest. Our
experiments are conducted on a dataset of 229,426 screening mammography exams
from 141,473 patients. We achieve an AUC of 0.8 on a test set consisting of 464
benign and 136 malignant lesions.
