A Meta-Learning Approach for Classifying Multimodal Retinal Images of Retinal Vein Occlusion With Limited Data
The proposed meta-learning models could be useful in automatically detecting RVO on CF and FA images.
Authors
- Xiaolin Xie (ORCID: https://orcid.org/0009-0002-3255-0341)
- Danqing Huang (ORCID: https://orcid.org/0000-0002-3112-0617)
- Danba Jiachu
- Hehua Ye (ORCID: https://orcid.org/0000-0002-6490-2244)
- L P Zhou (ORCID: https://orcid.org/0000-0001-5564-8750)
- Kebo Cai (ORCID: https://orcid.org/0000-0002-3330-9931)
- Mingzhi Zhang (ORCID: https://orcid.org/0000-0001-9032-7274)
- Meng Xie (ORCID: https://orcid.org/0009-0009-3071-3101)
- Gang Song (ORCID: https://orcid.org/0000-0002-3633-7284)
- Feng Jiang (ORCID: https://orcid.org/0000-0001-5362-3234)
- Ce Zheng (ORCID: https://orcid.org/0000-0001-7146-9138)
- Li Luo (ORCID: https://orcid.org/0000-0001-5519-3143)
- Jinming Guo
Institutions
- Chinese University of Hong Kong (HK)
- Shanghai Jiao Tong University (CN)
- XinHua Hospital (CN)
- Shantou University (CN)
- Shantou University Medical College (CN)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2024-09-19
- DOI
- https://doi.org/10.1167/tvst.13.9.22
- Citations
- 2
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- Shanghai Jiao Tong University
- Harvard Data Science Initiative, Harvard University
- School of Medicine, Shanghai Jiao Tong University