  Generative Adversarial Neural Networks (GANs) are applied to the synthetic
generation of prostate lesion MRI images. GANs have been applied to a variety
of natural images, is shown show that the same techniques can be used in the
medical domain to create realistic looking synthetic lesion images. 16mm x 16mm
patches are extracted from 330 MRI scans from the SPIE ProstateX Challenge 2016
and used to train a Deep Convolutional Generative Adversarial Neural Network
(DCGAN) utilizing cutting edge techniques. Synthetic outputs are compared to
real images and the implicit latent representations induced by the GAN are
explored. Training techniques and successful neural network architectures are
explained in detail.
