  We show that high quality, diverse and realistic-looking diffusion-weighted
magnetic resonance images can be synthesized using deep generative models.
Based on professional neuroradiologists' evaluations and diverse metrics with
respect to quality and diversity of the generated synthetic brain images, we
present two networks, the Introspective Variational Autoencoder and the
Style-Based GAN, that qualify for data augmentation in the medical field, where
information is saved in a dispatched and inhomogeneous way and access to it is
in many aspects restricted.
