  Mammography-based screening has helped reduce the breast cancer mortality
rate, but has also been associated with potential harms due to low specificity,
leading to unnecessary exams or procedures, and low sensitivity. Digital breast
tomosynthesis (DBT) improves on conventional mammography by increasing both
sensitivity and specificity and is becoming common in clinical settings.
However, deep learning (DL) models have been developed mainly on conventional
2D full-field digital mammography (FFDM) or scanned film images. Due to a lack
of large annotated DBT datasets, it is difficult to train a model on DBT from
scratch. In this work, we present methods to generalize a model trained on FFDM
images to DBT images. In particular, we use average histogram matching (HM) and
DL fine-tuning methods to generalize a FFDM model to the 2D maximum intensity
projection (MIP) of DBT images. In the proposed approach, the differences
between the FFDM and DBT domains are reduced via HM and then the base model,
which was trained on abundant FFDM images, is fine-tuned. When evaluating on
image patches extracted around identified findings, we are able to achieve
similar areas under the receiver operating characteristic curve (ROC AUC) of
$\sim 0.9$ for FFDM and $\sim 0.85$ for MIP images, as compared to a ROC AUC of
$\sim 0.75$ when tested directly on MIP images.
