A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms
This work addresses data deficiency and imbalance issues in mammogram classification for breast cancer diagnosis, representing an incremental advancement in domain-specific data augmentation methods.
The paper tackles the problem of improving BI-RADS classification of mammograms by proposing a novel transparency strategy-based data augmentation approach that generates more high-risk training examples, resulting in significant performance improvements and surpassing the state-of-the-art CutMix technique on three datasets.
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.