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The assessment of breast density is crucial in the context of breast cancerscreening, especially in populations with a higher percentage of dense breasttissues. This study introduces a novel data augmentation technique termedAttention-Guided Erasing (AGE), devised to enhance the downstreamclassification of four distinct breast density categories in mammographyfollowing the BI-RADS recommendation in the Vietnamese cohort. The proposedmethod integrates supplementary information during transfer learning, utilizingvisual attention maps derived from a vision transformer backbone trained usingthe self-supervised DINO method. These maps are utilized to erase backgroundregions in the mammogram images, unveiling only the potential areas of densebreast tissues to the network. Through the incorporation of AGE during transferlearning with varying random probabilities, we consistently surpassclassification performance compared to scenarios without AGE and thetraditional random erasing transformation. We validate our methodology usingthe publicly available VinDr-Mammo dataset. Specifically, we attain a meanF1-score of 0.5910, outperforming values of 0.5594 and 0.5691 corresponding toscenarios without AGE and with random erasing (RE), respectively. Thissuperiority is further substantiated by t-tests, revealing a p-value ofp<0.0001, underscoring the statistical significance of our approach.

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