Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.
Authors
Affiliations (11)
Affiliations (11)
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China.
- Wenzhou Medical University Wenzhou China.
- UCSC Baskin School of Engineering University of California Santa Cruz California USA.
- Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China.
- Department of Medicine Harvard Medical School and Brigham and Women's Hospital Boston Massachusetts USA.
- Joint Research Centre on Medicine The Affiliated Xiangshan Hospital of Wenzhou Medical University Ningbo China.
- Stony Brook University Stony Brook New York USA.
- School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China.
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China.
- Department of Big Data in Health Science The First Affiliated Hospital of Wenzhou Medical University Wenzhou China.
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization Wenzhou China.
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 categories: diagnostic classification of breast diseases, lesion segmentation and detection, and medical image generation. Additionally, the current public databases for mammography are summarized in detail. Finally, this paper analyzes the main challenges in the application of DL techniques in DBT, such as the lack of public datasets and model training issues, and proposes possible directions for future research, including large language models, multisource domain transfer, and data augmentation, to encourage innovative applications of DL in medical imaging.