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 p...
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| Format: | Article |
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Wiley
2025-06-01
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| Series: | MedComm |
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| Online Access: | https://doi.org/10.1002/mco2.70247 |
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| author | Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan |
| author_facet | Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan |
| author_sort | Ruoyun Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4e899ff362db437d9054a013e466aa3c |
| institution | Kabale University |
| issn | 2688-2663 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | MedComm |
| spelling | doaj-art-4e899ff362db437d9054a013e466aa3c2025-08-20T03:44:01ZengWileyMedComm2688-26632025-06-0166n/an/a10.1002/mco2.70247Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future TrendsRuoyun Wang0Fanxuan Chen1Haoman Chen2Chenxing Lin3Jincen Shuai4Yutong Wu5Lixiang Ma6Xiaoqu Hu7Min Wu8Jin Wang9Qi Zhao10Jianwei Shuai11Jingye Pan12Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaUCSC Baskin School of Engineering University of California Santa Cruz California USAWenzhou Medical University Wenzhou ChinaDepartment of Anatomy Histology & Embryology School of Basic Medical Sciences Fudan University Shanghai ChinaWenzhou Medical University Wenzhou ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou ChinaKey Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province Wenzhou ChinaABSTRACT 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.https://doi.org/10.1002/mco2.70247early breast cancer screeningdigital breast tomosynthesisdeep learningpublic databasemedical image analysis |
| spellingShingle | Ruoyun Wang Fanxuan Chen Haoman Chen Chenxing Lin Jincen Shuai Yutong Wu Lixiang Ma Xiaoqu Hu Min Wu Jin Wang Qi Zhao Jianwei Shuai Jingye Pan Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends MedComm early breast cancer screening digital breast tomosynthesis deep learning public database medical image analysis |
| title | Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends |
| title_full | Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends |
| title_fullStr | Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends |
| title_full_unstemmed | Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends |
| title_short | Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends |
| title_sort | deep learning in digital breast tomosynthesis current status challenges and future trends |
| topic | early breast cancer screening digital breast tomosynthesis deep learning public database medical image analysis |
| url | https://doi.org/10.1002/mco2.70247 |
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