FundusGAN: Fundus image synthesis based on semi-supervised learning

Abstract

Our goal is to construct a high-performance model that generates two types of fundus disease images for both Diabetic Retinopathy (DR) and Age-Related Macular degeneration (AMD) that have different symptoms for each severity for overcoming the lack of labeled data and data imbalance problems that hinder to build a model based on deep learning. To this end, we propose a framework named FundusGAN for building fundus image generators in semi-supervised learning. First, a semantic coarse lesion mask as a guidance mask is exploited to express disease and severity information more accurately to generate a final fundus image by adopting a coarse-to-fine strategy. Second, the disease-feature matching loss is proposed to learn the abundant features of the two diseases by employing the unlabeled datasets and is able to generate the initial symptoms of two fundus diseases accurately. We demonstrate the effectiveness of the proposed FundusGAN in comparison with carefully tuned baselines and state-of-the-art DR fundus image generation model and finally achieve 0.9174, and 0.8254 f1-score for DR and AMD, respectively.