Uncertainty‐guided cross‐level fusion network for retinal OCT image segmentation
Our proposed UGCFNet significantly advances retinal OCT segmentation by integrating uncertainty guidance and cross-level feature fusion, offering more reliable and accurate segmentation outcomes.
Authors
- Tao Peng (ORCID: https://orcid.org/0000-0003-0848-7901)
- Qing Peng (ORCID: https://orcid.org/0000-0002-6991-9432)
- Dehui Xiang (ORCID: https://orcid.org/0000-0001-7873-9778)
- Fei Shi (ORCID: https://orcid.org/0000-0002-8878-6655)
- Weifang Zhu (ORCID: https://orcid.org/0000-0001-9540-4101)
- Xinjian Chen (ORCID: https://orcid.org/0000-0002-0871-293X)
- Meng Wang (ORCID: https://orcid.org/0000-0001-7882-1747)
- Jiaxin Wang (ORCID: https://orcid.org/0000-0002-8432-7902)
Institutions
- Agency for Science, Technology and Research (SG)
- Institute of High Performance Computing (SG)
- Soochow University (CN)
- Shanghai Tenth People's Hospital (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/mp.18102
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Natural Science Foundation of Jiangsu Province