Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.

ABSTRACT: The high-resolution three-dimensional (3D) images generated with digital breast tomosynthesis (DBT) in the screening of breast cancer offer new possibilities for early disease diagnosis. Early detection is especially important as the incidence of breast cancer increases. However, DBT also presents challenges in terms of poorer results for dense breasts, increased false positive rates, slightly higher radiation doses, and increased reading times. Deep learning (DL) has been shown to effectively increase the processing efficiency and diagnostic accuracy of DBT images. This article reviews the application and outlook of DL in DBT-based breast cancer screening. First, the fundamentals and challenges of DBT technology are introduced. The applications of DL in DBT are then grouped into three cat

SUBMITTER: Wang R 

PROVIDER: S-EPMC12146671 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature