An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning
This model translates long-tail learning research into a practical grading tool for myopic maculopathy. It addresses data imbalance with the improved LTBSoftmax loss function, achieving high accuracy and efficiency. By enhancing feature extraction with ND Block, it provides reliable grading support for clinicians, especially in resource-limited settings.
Authors
- Weihua Yang (ORCID: https://orcid.org/0000-0002-7629-0193)
- M M Zhang
- Bo Zheng (ORCID: https://orcid.org/0000-0002-9654-1500)
- Wang Chen (ORCID: https://orcid.org/0000-0002-9606-365X)
- Lu Chen (ORCID: https://orcid.org/0000-0003-1141-8288)
- Shaojun Zhu (ORCID: https://orcid.org/0000-0001-7051-6342)
- Maonian Wu (ORCID: https://orcid.org/0000-0003-1014-8642)
- Tao Wu (ORCID: https://orcid.org/0009-0007-7864-6695)
Institutions
- Huzhou Normal University (CN)
- Southern Medical University Shenzhen Hospital (CN)
- Zhejiang University of Technology (CN)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1167/tvst.14.3.4
- Citations
- 2
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.72