Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866912395383799808 |
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| author | Tai, Cheng-Yu Chen, Ching-Wen Wu, Chi-Chin Chiu, Bo-Chen Cheng-Hung Lin Lu, Cheng-Kai Wang, Jia-Kang Huang, Tzu-Lun |
| author_facet | Tai, Cheng-Yu Chen, Ching-Wen Wu, Chi-Chin Chiu, Bo-Chen Cheng-Hung Lin Lu, Cheng-Kai Wang, Jia-Kang Huang, Tzu-Lun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases Tai, Cheng-Yu Chen, Ching-Wen Wu, Chi-Chin Chiu, Bo-Chen Cheng-Hung Lin Lu, Cheng-Kai Wang, Jia-Kang Huang, Tzu-Lun Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.20149 |