Digital breast tomosynthesis (DBT) enhances diagnostic accuracy by minimizing tissue overlap seen in digital mammography (DM). However, the substantial number of images generated by DBT poses challenges for radiologists. The aim of this study was to enhance the diagnostic accuracy and clinical utility of DM for breast cancer diagnosis by leveraging advanced feature representations derived from DBT.