  Breast cancer has the highest incidence and second highest mortality rate for
women in the US. Our study aims to utilize deep learning for benign/malignant
classification of mammogram tumors using a subset of cases from the Digital
Database of Screening Mammography (DDSM). Though it was a small dataset from
the view of Deep Learning (about 1000 patients), we show that currently state
of the art architectures of deep learning can find a robust signal, even when
trained from scratch. Using convolutional neural networks (CNNs), we are able
to achieve an accuracy of 85% and an ROC AUC of 0.91, while leading
hand-crafted feature based methods are only able to achieve an accuracy of 71%.
We investigate an amalgamation of architectures to show that our best result is
reached with an ensemble of the lightweight GoogLe Nets tasked with
interpreting both the coronal caudal view and the mediolateral oblique view,
simply averaging the probability scores of both views to make the final
prediction. In addition, we have created a novel method to visualize what
features the neural network detects for the benign/malignant classification,
and have correlated those features with well known radiological features, such
as spiculation. Our algorithm significantly improves existing classification
methods for mammography lesions and identifies features that correlate with
established clinical markers.
