Personalized echocardiographic segmentation via bidirectional encoder representations from transformers Y‐shaped network with patient attributes
BTY-Net offers an effective and interpretable solution for personalized echocardiographic analysis. By leveraging multimodal fusion of patient information and image data, it enhances segmentation accuracy, and embeds clinically meaningful attention maps, thereby delivering a multimodal, sex-robust and clinically interpretable solution for routine echocardiographic analysis.
Authors
- Qi Zhang (ORCID: https://orcid.org/0000-0001-9595-5640)
- Zhonglin Lu
- Chuiguo Huang
- Jingzhang Sun
- Chunhao Li
- Jiahui Dong
- Chunlei Liu
- Yu Du
- Chunjie Cao
Institutions
- Army Medical University (CN)
- Chinese University of Hong Kong (HK)
- University of Michigan (US)
- Michigan Medicine (US)
- Prince of Wales Hospital (CN)
- Ann Arbor VA Medical Center (US)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Southwest Hospital (CN)
- City University of Macau (MO)
- Hainan Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-12-21
- DOI
- https://doi.org/10.1002/mp.70235
- Primary Topic
- Phonocardiography and Auscultation Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China