  Skin cancer is by far the most common type of cancer. Early detection is the
key to increase the chances for successful treatment significantly. Currently,
Deep Neural Networks are the state-of-the-art results on automated skin cancer
classification. To push the results further, we need to address the lack of
annotated data, which is expensive and require much effort from specialists. To
bypass this problem, we propose using Generative Adversarial Networks for
generating realistic synthetic skin lesion images. To the best of our
knowledge, our results are the first to show visually-appealing synthetic
images that comprise clinically-meaningful information.
