Paper ID: 2203.05344

EyeLoveGAN: Exploiting domain-shifts to boost network learning with cycleGANs

Josefine Vilsbøll Sundgaard, Kristine Aavild Juhl, Jakob Mølkjær Slipsager

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.

Submitted: Mar 10, 2022