  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.
