  Breast cancer is the malignant tumor that causes the highest number of cancer
deaths in females. Digital mammograms (DM or 2D mammogram) and digital breast
tomosynthesis (DBT or 3D mammogram) are the two types of mammography imagery
that are used in clinical practice for breast cancer detection and diagnosis.
Radiologists usually read both imaging modalities in combination; however,
existing computer-aided diagnosis tools are designed using only one imaging
modality. Inspired by clinical practice, we propose an innovative convolutional
neural network (CNN) architecture for breast cancer classification, which uses
both 2D and 3D mammograms, simultaneously. Our experiment shows that the
proposed method significantly improves the performance of breast cancer
classification. By assembling three CNN classifiers, the proposed model
achieves 0.97 AUC, which is 34.72% higher than the methods using only one
imaging modality.
