  Data augmentation is essential for medical research to increase the size of
training datasets and achieve better results. In this work, we experiment three
GAN architectures with different loss functions to generate new brain MRIs. The
results show the importance of hyperparameter tuning and the use of mini-batch
similarity layer in the Discriminator and gradient penalty in the loss function
to achieve convergence with high quality and realism. Moreover, huge
computation time is needed to generate indistinguishable images from the
original dataset.

Image and Video Processing;Machine Learning;Machine Learning