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Retinal Image Synthesis and Semi-supervised Learning for Glaucoma Assessment
2019
IEEE Transactions on Medical Imaging
Recent works show that generative adversarial networks (GANs) can be successfully applied to image synthesis and semi-supervised learning, where, given a small labeled database and a large unlabeled database, the goal is to train a powerful classifier. In this paper, we trained a retinal image synthesizer and a semi-supervised learning method for automatic glaucoma assessment using an adversarial model on a small glaucoma-labeled database and a large unlabeled database. Various studies have
doi:10.1109/tmi.2019.2903434
pmid:30843823
fatcat:2rq7d3uag5gzdjc6uecacdvqya