A Novel Transparency Strategy-based Data Augmentation Approach for BI-RADS Classification of Mammograms

TL;DR

This paper introduces a transparency-based data augmentation method that leverages ROI information to enhance BI-RADS classification accuracy in mammogram analysis, outperforming existing augmentation techniques.

Contribution

The novel transparency strategy effectively generates high-risk training samples using ROI data, improving mammogram classification beyond current state-of-the-art methods.

Findings

Significant performance improvement over existing augmentation methods.

Outperforms CutMix in BI-RADS classification tasks.

Effective across multiple datasets.

Abstract

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.