This paper focuses on using few-shot learning to improve the accuracy of classifying OCT diagnosis images with major and rare classes. We used the GAN-based augmentation strategy as a baseline and introduced several novel methods to further enhance our model. The proposed strategy contains U-GAT-IT for improving the generative part and uses the data balance technique to narrow down the skew of accuracy between all categories. The best model obtained was built with CBAM attention mechanism and fine-tuned InceptionV3, and achieved an overall accuracy of 97.85%, representing a significant improvement over the original baseline.
View on arXiv@article{tai2025_2505.20149,
title={ Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases },
author={ Cheng-Yu Tai and Ching-Wen Chen and Chi-Chin Wu and Bo-Chen Chiu and Cheng-Hung and Cheng-Kai Lu and Jia-Kang Wang and Tzu-Lun Huang },
journal={arXiv preprint arXiv:2505.20149},
year={ 2025 }
}