A fundamental problem in employing deep learning algorithms in the medical field is the lack of labeled data and severe class imbalance. In this work, we present novel ways to enlarge small scale datasets. We introduce an autoencoder framework comprised of an encoder and a StyleGAN generator to embed images into the latent space of StyleGAN. The autoencoder learns the disentangled latent representation of the data allowing for encoding real images to the latent space and manipulating the latent vector in a meaningful manner. We suggest ways to use the encoder along with the unique architecture of the StyleGAN generator to control the synthesized images and thus, create class-specific images that can be used to train and improve existing deep learning algorithms.