  Purpose: To develop and evaluate the accuracy of a multi-view deep learning
approach to the analysis of high-resolution synthetic mammograms from digital
breast tomosynthesis screening cases, and to assess the effect on accuracy of
image resolution and training set size. Materials and Methods: In a
retrospective study, 21,264 screening digital breast tomosynthesis (DBT) exams
obtained at our institution were collected along with associated radiology
reports. The 2D synthetic mammographic images from these exams, with varying
resolutions and data set sizes, were used to train a multi-view deep
convolutional neural network (MV-CNN) to classify screening images into BI-RADS
classes (BI-RADS 0, 1 and 2) before evaluation on a held-out set of exams.
  Results: Area under the receiver operating characteristic curve (AUC) for
BI-RADS 0 vs non-BI-RADS 0 class was 0.912 for the MV-CNN trained on the full
dataset. The model obtained accuracy of 84.8%, recall of 95.9% and precision of
95.0%. This AUC value decreased when the same model was trained with 50% and
25% of images (AUC = 0.877, P=0.010 and 0.834, P=0.009 respectively). Also, the
performance dropped when the same model was trained using images that were
under-sampled by 1/2 and 1/4 (AUC = 0.870, P=0.011 and 0.813, P=0.009
respectively).
  Conclusion: This deep learning model classified high-resolution synthetic
mammography scans into normal vs needing further workup using tens of thousands
of high-resolution images. Smaller training data sets and lower resolution
images both caused significant decrease in performance.
