  Generative adversarial networks (GANs) are a class of unsupervised machine
learning algorithms that can produce realistic images from randomly-sampled
vectors in a multi-dimensional space. Until recently, it was not possible to
generate realistic high-resolution images using GANs, which has limited their
applicability to medical images that contain biomarkers only detectable at
native resolution. Progressive growing of GANs is an approach wherein an image
generator is trained to initially synthesize low resolution synthetic images
(8x8 pixels), which are then fed to a discriminator that distinguishes these
synthetic images from real downsampled images. Additional convolutional layers
are then iteratively introduced to produce images at twice the previous
resolution until the desired resolution is reached. In this work, we
demonstrate that this approach can produce realistic medical images in two
different domains; fundus photographs exhibiting vascular pathology associated
with retinopathy of prematurity (ROP), and multi-modal magnetic resonance
images of glioma. We also show that fine-grained details associated with
pathology, such as retinal vessels or tumor heterogeneity, can be preserved and
enhanced by including segmentation maps as additional channels. We envisage
several applications of the approach, including image augmentation and
unsupervised classification of pathology.
