  Retinal artery/vein (A/V) classification is a critical technique for
diagnosing diabetes and cardiovascular diseases. Although deep learning based
methods achieve impressive results in A/V classification, their performances
usually degrade severely when being directly applied to another database, due
to the domain shift, e.g., caused by the variations in imaging protocols. In
this paper, we propose a novel vessel-mixing based consistency regularization
framework, for cross-domain learning in retinal A/V classification. Specially,
to alleviate the severe bias to source domain, based on the label smooth prior,
the model is regularized to give consistent predictions for unlabeled
target-domain inputs that are under perturbation. This consistency
regularization implicitly introduces a mechanism where the model and the
perturbation is opponent to each other, where the model is pushed to be robust
enough to cope with the perturbation. Thus, we investigate a more difficult
opponent to further inspire the robustness of model, in the scenario of retinal
A/V, called vessel-mixing perturbation. Specially, it effectively disturbs the
fundus images especially the vessel structures by mixing two images regionally.
We conduct extensive experiments on cross-domain A/V classification using four
public datasets, which are collected by diverse institutions and imaging
devices. The results demonstrate that our method achieves the state-of-the-art
cross-domain performance, which is also close to the upper bound obtained by
fully supervised learning on target domain.
