Weakly supervised deep learning for multimodal MRI-TRUS registration: Toward assisting prostate biopsy guidance
The proposed weakly supervised framework achieves high-precision MRI-TRUS registration with minimal annotation, ensuring strong generalizability and clinical applicability.
Authors
- Ruigang Huang
- Jiayi Wang
- Hongmei Mi (ORCID: https://orcid.org/0009-0002-5398-4106)
- Binggang Xiao (ORCID: https://orcid.org/0000-0001-6920-101X)
- Hanzong Lin
- Yingying Ding
- E.H. Wang (ORCID: https://orcid.org/0009-0005-6357-5340)
- Guorong Lyu (ORCID: https://orcid.org/0000-0003-3123-1138)
- Chenjie Xu
- Xia Li (ORCID: https://orcid.org/0009-0007-5761-5283)
- Ningjie Huang
- Dongping Zhang (ORCID: https://orcid.org/0000-0001-6743-8945)
- You Zhou (ORCID: https://orcid.org/0000-0001-9474-9087)
- Furong Luo
- Jiabin Yu (ORCID: https://orcid.org/0009-0009-9074-1665)
- Li Yang (ORCID: https://orcid.org/0000-0003-1878-5013)
- Ming Chen
Institutions
- Fujian Medical University (CN)
- Wenzhou Medical University (CN)
- Zhangzhou Municipal Hospital of Fujian Province (CN)
- Shanghai East Hospital (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
- Zhejiang Taizhou Hospital (CN)
- China Jiliang University (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251375870
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00