Self-Supervised Domain Adaptation for Diabetic Retinopathy Grading using Vessel Image Reconstruction

Ho Minh Duy Nguyen, Truong Thanh-Nhat Mai, Ngoc Trong Tuong Than, Alexander Prange, Daniel Sonntag

In: Proceedings of the 44th German Conference on Artificial Intelligence. German Conference on Artificial Intelligence (KI-2021) September 27-October 1 Berlin/Virtual Germany LNCS Springer 2021.


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.


KI_2021_Self_Supervised_Domain_Adaptation_for_Diabetic_Retinopathy_Grading.pdf (pdf, 2 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz