  Automated methods for breast cancer detection have focused on 2D mammography
and have largely ignored 3D digital breast tomosynthesis (DBT), which is
frequently used in clinical practice. The two key challenges in developing
automated methods for DBT classification are handling the variable number of
slices and retaining slice-to-slice changes. We propose a novel deep 2D
convolutional neural network (CNN) architecture for DBT classification that
simultaneously overcomes both challenges. Our approach operates on the full
volume, regardless of the number of slices, and allows the use of pre-trained
2D CNNs for feature extraction, which is important given the limited amount of
annotated training data. In an extensive evaluation on a real-world clinical
dataset, our approach achieves 0.854 auROC, which is 28.80% higher than
approaches based on 3D CNNs. We also find that these improvements are stable
across a range of model configurations.
