Exploring Drusen Type and Appearance using Interpretable {GANs}

We propose an algorithmic pipeline that uses interpretable Generative Adversarial Networks (GANs) to visualize the variability of the visual appearance of drusen in Optical Coherence Tomography (OCT). Drusen are accumulations of extracellular debris between Bruch’s membrane and the retinal pigment epithelium of the eye. They are a hallmark of age-related macular degeneration (AMD)-the most common cause of vision loss in the elderly. Imaging the morphology of drusen with OCT reveals different subtypes, which might have different relevance for disease severity and the risk of progression. We compare two GAN architectures and three recently proposed methods for the unsupervised discovery of interpretable paths in their latent space with respect to their ability to visualize natural variations in drusen appearance. We also introduce a color code that indicates generated images that extrapolate beyond the training data and should, therefore, be interpreted with caution. Our results suggest that, even when trained on cross-sectional data, GANs can recover smooth and anatomically plausible variations of drusen that are in agreement with changes over time that are known from longitudinal observations.