  Image augmentation techniques have been widely investigated to improve the
performance of deep learning (DL) algorithms on mammography classification
tasks. Recent methods have proved the efficiency of image augmentation on data
deficiency or data imbalance issues. In this paper, we propose a novel
transparency strategy to boost the Breast Imaging Reporting and Data System
(BI-RADS) scores of mammogram classifiers. The proposed approach utilizes the
Region of Interest (ROI) information to generate more high-risk training
examples for breast cancer (BI-RADS 3, 4, 5) from original images. Our
extensive experiments on three different datasets show that the proposed
approach significantly improves the mammogram classification performance and
surpasses a state-of-the-art data augmentation technique called CutMix. This
study also highlights that our transparency method is more effective than other
augmentation strategies for BI-RADS classification and can be widely applied to
other computer vision tasks.
