  In Fluorescein Angiography (FA), an exogenous dye is injected in the
bloodstream to image the vascular structure of the retina. The injected dye can
cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even
death. In contrast, color fundus imaging is a non-invasive technique used for
photographing the retina but does not have sufficient fidelity for capturing
its vascular structure. The only non-invasive method for capturing retinal
vasculature is optical coherence tomography-angiography (OCTA). However, OCTA
equipment is quite expensive, and stable imaging is limited to small areas on
the retina. In this paper, we propose a novel conditional generative
adversarial network (GAN) capable of simultaneously synthesizing FA images from
fundus photographs while predicting retinal degeneration. The proposed system
has the benefit of addressing the problem of imaging retinal vasculature in a
non-invasive manner as well as predicting the existence of retinal
abnormalities. We use a semi-supervised approach to train our GAN using
multiple weighted losses on different modalities of data. Our experiments
validate that the proposed architecture exceeds recent state-of-the-art
generative networks for fundus-to-angiography synthesis. Moreover, our vision
transformer-based discriminators generalize quite well on out-of-distribution
data sets for retinal disease prediction.
