This paper investigates the problem of domain adaptation for diabetic retinopathy (DR) grading. We learn invariant target-domain features by defining a novel self-supervised task based on retinal vessel image reconstructions, inspired by medical domain knowledge. Then, a benchmark of current state-of-the-art unsupervised domain adaptation methods on the DR problem is provided. It can be shown that our approach outperforms existing domain adaption strategies. Furthermore, when utilizing entire training data in the target domain, we are able to compete with several state-of-the-art approaches in final classification accuracy just by applying standard network architectures and using image-level labels.

翻译：本文探讨了糖尿病视网膜病(DR)分级的域适应问题。 我们通过在医学领域知识的启发下,根据视网膜容器图像重建,界定了一种新的自我监督任务,学习了无变目标域特征。 然后,提供了当前最先进的无监控域适应DR问题方法的基准。可以表明,我们的方法优于现有域适应战略。此外,在使用目标领域的整个培训数据时,我们能够仅仅通过应用标准网络架构和使用图像等级标签,在最终分类精度方面与一些最先进的方法竞争。