Synthetic Medical Images from Dual Generative Adversarial Networks
This addresses the data accessibility issue for medical image classification researchers, potentially enabling public use of previously private data, though it appears incremental as it builds on existing GAN methods.
The paper tackles the problem of scarce and private medical imaging data by proposing a two-stage pipeline using dual generative adversarial networks to generate synthetic retinal fundi images, dividing the task into geometry and photorealism components.
Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns regarding patient privacy. Typical consent forms only allow for patient data to be used in medical journals or education, meaning the majority of medical data is inaccessible for general public research. We propose a novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images. We develop a hierarchical generation process to divide the complex image generation task into two parts: geometry and photorealism. We hope researchers will use our pipeline to bring private medical data into the public domain, sparking growth in imaging tasks that have previously relied on the hand-tuning of models. We have begun this initiative through the development of SynthMed, an online repository for synthetic medical images.