用改进的Swim Transformer提升糖尿病视网膜病变早期检测准确率
Enhancing DR Classification with Swin Transformer and Shifted Window Attention
- 采用分层令牌处理与移位窗口注意力捕捉细粒度特征
- 在Aptos和IDRiD数据集上分别达到89.65%和97.40%准确率
- 适合医疗影像自动化筛查场景，尤其擅长早期病变识别
糖尿病视网膜病变是全球致盲的主要原因，早期检测对治疗至关重要。然而，图像质量差异、类别不平衡以及像素级相似性给自动化分类带来挑战。为此，我们提出一种鲁棒的预处理流程，包含图像裁剪、限制对比度自适应直方图均衡化（CLAHE）及针对性数据增强，以提升模型泛化能力。方法基于Swin Transformer，利用分层令牌处理与移位窗口注意力，在保持线性计算复杂度的同时高效捕获细粒度特征。在Aptos和IDRiD数据集上进行多类DR分类验证，准确率分别为89.65%和97.40%。结果表明该模型在早期DR检测中尤为有效，具备临床自动视网膜筛查应用潜力。
原文摘要 · Abstract (English)
Diabetic retinopathy (DR) is a leading cause of blindness worldwide, underscoring the importance of early detection for effective treatment. However, automated DR classification remains challenging due to variations in image quality, class imbalance, and pixel-level similarities that hinder model training. To address these issues, we propose a robust preprocessing pipeline incorporating image cropping, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and targeted data augmentation to improve model generalization and resilience. Our approach leverages the Swin Transformer, which utilizes hierarchical token processing and shifted window attention to efficiently capture fine-grained features while maintaining linear computational complexity. We validate our method on the Aptos and IDRiD datasets for multi-class DR classification, achieving accuracy rates of 89.65% and 97.40%, respectively. These results demonstrate the effectiveness of our model, particularly in detecting early-stage DR, highlighting its potential for improving automated retinal screening in clinical settings.
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