  Generative Adversarial Networks (GANs) have been successfully used to
synthesize realistically looking images of faces, scenery and even medical
images. Unfortunately, they usually require large training datasets, which are
often scarce in the medical field, and to the best of our knowledge GANs have
been only applied for medical image synthesis at fairly low resolution.
However, many state-of-the-art machine learning models operate on high
resolution data as such data carries indispensable, valuable information. In
this work, we try to generate realistically looking high resolution images of
skin lesions with GANs, using only a small training dataset of 2000 samples.
The nature of the data allows us to do a direct comparison between the image
statistics of the generated samples and the real dataset. We both
quantitatively and qualitatively compare state-of-the-art GAN architectures
such as DCGAN and LAPGAN against a modification of the latter for the task of
image generation at a resolution of 256x256px. Our investigation shows that we
can approximate the real data distribution with all of the models, but we
notice major differences when visually rating sample realism, diversity and
artifacts. In a set of use-case experiments on skin lesion classification, we
further show that we can successfully tackle the problem of heavy class
imbalance with the help of synthesized high resolution melanoma samples.
