Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classification

TL;DR

This paper proposes Attention-Guided Erasing (AGE), a novel data augmentation method that uses visual attention maps from a vision transformer to improve breast density classification accuracy in mammography, validated on the VinDr-Mammo dataset.

Contribution

The study introduces AGE, a new augmentation technique leveraging attention maps to enhance transfer learning for breast density classification.

Findings

AGE improves classification performance over traditional methods.

The method achieves a higher mean F1-score of 0.5910.

Statistical tests confirm the significance of improvements.

Abstract

The assessment of breast density is crucial in the context of breast cancer screening, especially in populations with a higher percentage of dense breast tissues. This study introduces a novel data augmentation technique termed Attention-Guided Erasing (AGE), devised to enhance the downstream classification of four distinct breast density categories in mammography following the BI-RADS recommendation in the Vietnamese cohort. The proposed method integrates supplementary information during transfer learning, utilizing visual attention maps derived from a vision transformer backbone trained using the self-supervised DINO method. These maps are utilized to erase background regions in the mammogram images, unveiling only the potential areas of dense breast tissues to the network. Through the incorporation of AGE during transfer learning with varying random probabilities, we consistently surpass classification performance compared to scenarios without AGE and the traditional random erasing transformation. We validate our methodology using the publicly available VinDr-Mammo dataset. Specifically, we attain a mean F1-score of 0.5910, outperforming values of 0.5594 and 0.5691 corresponding to scenarios without AGE and with random erasing (RE), respectively. This superiority is further substantiated by t-tests, revealing a p-value of p<0.0001, underscoring the statistical significance of our approach.