A multi-scale pyramid residual weight network for medical image fusion
LYWNet excels at integrating high-frequency detail information and low-frequency contextual information, addressing the deficiencies of existing DL-based image fusion methods. This approach provides superior fused images that retain the functional metabolic information and anatomical texture, making it a valuable tool for clinical diagnosis and surgical navigation.
Authors
- Shaoze Zhang (ORCID: https://orcid.org/0000-0001-9387-2462)
- Yiwei Liu (ORCID: https://orcid.org/0000-0003-0633-3941)
- Yao Tang (ORCID: https://orcid.org/0000-0001-9028-5959)
- Zuo‐Xiang He
- Xihai Zhao
Institutions
- Beijing Tsinghua Chang Gung Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2025-03-01
- DOI
- https://doi.org/10.21037/qims-24-851
- Citations
- 2
- Primary Topic
- Advanced Image Fusion Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.27