Mamba‐Convolutional UNet for multi‐modal medical image synthesis
The proposed Mamba-Convolutional UNet features a dual-branch structure that effectively combines global and local features for enhanced medical image understanding. And the Mamba block's reprogramming layer addresses challenges in target modality transformation during insufficient training.
Authors
- Yu Luo (ORCID: https://orcid.org/0000-0003-3968-9725)
- Shun Yao (ORCID: https://orcid.org/0000-0003-1727-4078)
- Fenghuan Li (ORCID: https://orcid.org/0000-0003-3640-4253)
- Zhichao Yin (ORCID: https://orcid.org/0000-0002-3458-7909)
- Jie Ling (ORCID: https://orcid.org/0000-0002-9535-608X)
- Jing Qin
- Wenlong Lin
Institutions
- Guangdong University of Technology (CN)
- Hong Kong Polytechnic University (HK)
- Sun Yat-sen University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Guangzhou Electronic Technology (China) (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1002/mp.70029
- Citations
- 2
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.55