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.

翻译：乳腺密度评估在乳腺癌筛查中至关重要，尤其在致密型乳腺组织占比较高的人群中。本研究提出一种名为"注意力引导擦除"（AGE）的新型数据增强技术，旨在依据BI-RADS指南对越南队列中四种乳腺密度类别进行下游分类的优化。该方法在迁移学习过程中整合补充信息，利用基于自监督DINO方法训练的视觉Transformer骨干网络生成的视觉注意力图。这些注意力图用于擦除乳腺X线影像中的背景区域，仅向网络暴露潜在致密乳腺组织区域。通过在不同随机概率下将AGE纳入迁移学习，我们始终比未使用AGE或传统随机擦除变换的场景获得更优的分类性能。我们使用公开的VinDr-Mammo数据集验证该方法，具体实现了0.5910的平均F1分数，超过未使用AGE（0.5594）和随机擦除（0.5691）的对应数值。该优越性通过t检验得到进一步证实，p<0.0001，凸显了方法的统计显著性。