Generative Modeling for Retinal Fundus Image Synthesis
Medical imaging datasets typically do not contain many training images, usually being deficient for training deep learning networks.
We propose a deep residual variational auto-encoder and a generative adversarial network that can generate a synthetic retinal fundus image dataset with corresponding blood vessel annotation. Our
initial experiments produce results with higher scores than the state
of the art for verifying that the structural statistics of our generated
images are compatible with real fundus images. The successful application of generative models to generate synthetic medical data
will not only help to mitigate the small dataset problem but will also
address the privacy concerns associated with medical datasets.