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

Journal: MedComm
Published Date:

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.

Authors

  • Ruoyun Wang
    Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China.
  • Fanxuan Chen
    School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
  • Haoman Chen
    Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China.
  • Chenxing Lin
    Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou China.
  • Jincen Shuai
    Baskin Engineering, University of California, Santa Cruz, CA, United States.
  • Yutong Wu
    The Australian e-Health Research Centre, CSIRO, Brisbane, Australia.
  • Lixiang Ma
    Department of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai China.
  • Xiaoqu Hu
    Department of Surgical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Min Wu
    Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China.
  • Jin Wang
    Cells Vision (Guangzhou) Medical Technology Inc., Guangzhou, China. Electronic address: wangjin@cellsvision.com.
  • Qi Zhao
  • Jianwei Shuai
    Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen 361005, China; Wenzhou Institute, University of Chinese Academy of Sciences, and Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang 325001, China; National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen 361102, China. Electronic address: jianweishuai@xmu.edu.cn.
  • Jingye Pan
    Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou China.

Keywords

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