  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.
