  Fluorescein angiography can provide a map of retinal vascular structure and
function, which is commonly used in ophthalmology diagnosis, however, this
imaging modality may pose risks of harm to the patients. To help physicians
reduce the potential risks of diagnosis, an image translation method is
adopted. In this work, we proposed a conditional generative adversarial
network(GAN) - based method to directly learn the mapping relationship between
structure fundus images and fundus fluorescence angiography images. Moreover,
local saliency maps, which define each pixel's importance, are used to define a
novel saliency loss in the GAN cost function. This facilitates more accurate
learning of small-vessel and fluorescein leakage features.
