  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.
