  One of the leading causes of blindness is glaucoma, which is challenging to
detect since it remains asymptomatic until the symptoms are severe. Thus,
diagnosis is usually possible until the markers are easy to identify, i.e., the
damage has already occurred. Early identification of glaucoma is generally made
based on functional, structural, and clinical assessments. However, due to the
nature of the disease, researchers still debate which markers qualify as a
consistent glaucoma metric. Deep learning methods have partially solved this
dilemma by bypassing the marker identification stage and analyzing high-level
information directly to classify the data. Although favorable, these methods
make expert analysis difficult as they provide no insight into the model
discrimination process. In this paper, we overcome this using deep generative
networks, a deep learning model that learns complicated, high-dimensional
probability distributions. We train a Deep Feature consistent Variational
Autoencoder (DFC-VAE) to reconstruct optic disc images. We show that a
small-sized latent space obtained from the DFC-VAE can learn the
high-dimensional glaucoma data distribution and provide discriminatory evidence
between normal and glaucoma eyes. Latent representations of size as low as 128
from our model got a 0.885 area under the receiver operating characteristic
curve when trained with Support Vector Classifier.
