  Diabetic retinopathy (DR) is one of the leading causes of blindness in the
working-age population of developed countries, caused by a side effect of
diabetes that reduces the blood supply to the retina. Deep neural networks have
been widely used in automated systems for DR classification on eye fundus
images. However, these models need a large number of annotated images. In the
medical domain, annotations from experts are costly, tedious, and
time-consuming; as a result, a limited number of annotated images are
available. This paper presents a semi-supervised method that leverages
unlabeled images and labeled ones to train a model that detects diabetic
retinopathy. The proposed method uses unsupervised pretraining via
self-supervised learning followed by supervised fine-tuning with a small set of
labeled images and knowledge distillation to increase the performance in
classification task. This method was evaluated on the EyePACS test and
Messidor-2 dataset achieving 0.94 and 0.89 AUC respectively using only 2% of
EyePACS train labeled images.
