用排序感知提示提升眼底图像糖尿病视网膜病变分级准确率
Diabetic Retinopathy Grading with CLIP-based Ranking-Aware Adaptation:A Comparative Study on Fundus Image
- 设计排序感知提示，捕捉病变严重程度的有序性
- 最高准确率达93.42%，重症病例召回率显著提升
- 适合临床筛查场景，对重症检测尤为可靠
糖尿病视网膜病变（DR）是可预防失明的主要原因，自动化眼底图像分级在大规模筛查中具有重要意义。本文研究三种基于CLIP的方法用于五级DR严重程度分级：(1) 使用提示工程的零样本基线，(2) 增强CBAM注意力的混合全卷积网络-CLIP模型，(3) 编码DR进展顺序结构的排序感知提示模型。在合并APTOS 2019与Messidor-2数据集（n=5,406）上进行训练与评估，通过重采样和类别特异性最优阈值处理类别不平衡问题。实验表明，排序感知模型达到最高总体准确率（93.42%，AUROC 0.9845），且对临床关键的重症病例具有强召回能力；混合FCN-CLIP模型（92.49%，AUROC 0.99）在检测增殖性DR方面表现优异。两者均显著优于零样本基线（55.17%，AUROC 0.75）。分析各方法互补优势，并讨论其在筛查场景中的实际意义。
原文摘要 · Abstract (English)
Diabetic retinopathy (DR) is a leading cause of preventable blindness, and automated fundus image grading can play an important role in large-scale screening. In this work, we investigate three CLIP-based approaches for five-class DR severity grading: (1) a zero-shot baseline using prompt engineering, (2) a hybrid FCN-CLIP model augmented with CBAM attention, and (3) a ranking-aware prompting model that encodes the ordinal structure of DR progression. We train and evaluate on a combined dataset of APTOS 2019 and Messidor-2 (n=5,406), addressing class imbalance through resampling and class-specific optimal thresholding. Our experiments show that the ranking-aware model achieves the highest overall accuracy (93.42%, AUROC 0.9845) and strong recall on clinically critical severe cases, while the hybrid FCN-CLIP model (92.49%, AUROC 0.99) excels at detecting proliferative DR. Both substantially outperform the zero-shot baseline (55.17%, AUROC 0.75). We analyze the complementary strengths of each approach and discuss their practical implications for screening contexts.
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