用生成增强与注意力机制提升罕见眼病的少量样本分类准确率
Improvement Strategies for Few-Shot Learning in OCT Image Classification of Rare Retinal Diseases
- 结合U-GAT-IT生成器与数据平衡技术优化罕见类样本
- 引入CBAM注意力和微调InceptionV3，整体准确率达97.85%
- 适合医疗图像少样本学习研究者参考
本文针对光学相干断层扫描（OCT）图像中常见与罕见视网膜疾病分类问题，采用少样本学习方法提升分类精度。以基于GAN的数据增强为基线，提出新策略：使用U-GAT-IT改进生成部分，并引入数据平衡技术缓解各类别间准确率偏差。最优模型结合CBAM注意力机制与微调后的InceptionV3，整体准确率达到97.85%，显著优于原始基线。
原文摘要 · Abstract (English)
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.
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