  This paper presents our contribution to the REFUGE challenge 2020. The
challenge consisted of three tasks based on a dataset of retinal images:
Segmentation of optic disc and cup, classification of glaucoma, and
localization of fovea. We propose employing convolutional neural networks for
all three tasks. Segmentation is performed using a U-Net, classification is
performed by a pre-trained InceptionV3 network, and fovea detection is
performed by employing stacked hour-glass for heatmap prediction. The challenge
dataset contains images from three different data sources. To enhance
performance, cycleGANs were utilized to create a domain-shift between the data
sources. These cycleGANs move images across domains, thus creating artificial
images which can be used for training.
