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.
Read more on our methods to generalize a model trained on FFDM images to DBT images. We are able to achieve similar areas under the receiver operating characteristic curve (ROC AUC) of ∼0.9 for FFDM and ∼0.85 for MIP images, as compared to a ROC AUC of ∼0.75 when tested directly on MIP images.