Transparency strategy-based data augmentation for BI-RADS classification of mammograms

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 mammograms classifier. The proposed approach utilizes the Region of Interest (ROI) information to generate more high-risk training examples from original images. Our extensive experiments were conducted on our benchmark mammography dataset. The experiment results show that the proposed approach surpasses current state-of-the-art data augmentation techniques such as Upsampling or CutMix. The study highlights that the transparency method is more effective than other augmentation strategies for BI-RADS classification and can be widely applied for our computer vision tasks.