Multiloss strategy for breast cancer subtype classification using digital breast tomosynthesis

Abstract

AbstractBreast cancer is the most common cancer for women, and it also is the leading cause of cancer‐related deaths. As a highly heterogeneous disease, it is crucial to identify the molecular subtypes of breast cancer before individualized therapy. Therefore, we proposed a multiloss network framework to classify the breast cancer subtype based on digital breast tomosynthesis (DBT) images. We employed the multiloss strategy to learn the low‐level features more efficiently and effectively, which would provide a better basis for extracting the high‐level features. Additionally, we also proposed a decomposed attention block (DA), which not only captured the interdependencies between all channels but also the precise positional information at the x‐ and y‐dimensions. The multiloss strategy enables the network to learn useful feature representations of breast cancer subtypes, and the DA block further improves the classification performance by capturing the information between channels and positions.