Domain adaptation (DA) has been widely applied in the diabetic retinopathy (DR) grading of unannotated ultra-wide-field (UWF) fundus images, which can transfer annotated knowledge from labeled color fundus images. However, suffering from huge domain gaps and complex real-world scenarios, the DR grading performance of most mainstream DA is far from that of clinical diagnosis. To tackle this, we propose a novel source-free active domain adaptation (SFADA) in this paper. Specifically, we focus on DR grading problem itself and propose to generate features of color fundus images with continuously evolving relationships of DRs, actively select a few valuable UWF fundus images for labeling with local representation matching, and adapt model on UWF fundus images with DR lesion prototypes. Notably, the SFADA also takes data privacy and computational efficiency into consideration. Extensive experimental results demonstrate that our proposed SFADA achieves state-of-the-art DR grading performance, increasing accuracy by 20.9% and quadratic weighted kappa by 18.63% compared with baseline and reaching 85.36% and 92.38% respectively. These investigations show that the potential of our approach for real clinical practice is promising.

翻译：域适应（DA）已广泛应用于未标注超广角（UWF）眼底图像的糖尿病视网膜病变（DR）分级，能够将有标注的彩色眼底图像知识迁移至目标域。然而，受制于巨大的域间隙和复杂的现实场景，大多数主流域适应方法的DR分级性能远未达到临床诊断水平。为解决这一问题，本文提出一种新颖的无源主动域适应（SFADA）方法。具体而言，我们聚焦于DR分级问题本身，提出通过DR持续演化关系生成彩色眼底图像特征，利用局部表征匹配主动选取少量有价值的UWF眼底图像进行标注，并借助DR病灶原型在UWF眼底图像上自适应模型。值得注意的是，SFADA同时考虑了数据隐私与计算效率。大量实验结果表明，本文提出的SFADA方法取得了最先进的DR分级性能，与基线相比准确率提升20.9%，二次加权卡帕系数提升18.63%，分别达到85.36%和92.38%。这些研究表明，该方法在实际临床实践中具有巨大潜力。