  As many other machine learning driven medical image analysis tasks, skin
image analysis suffers from a chronic lack of labeled data and skewed class
distributions, which poses problems for the training of robust and
well-generalizing models. The ability to synthesize realistic looking images of
skin lesions could act as a reliever for the aforementioned problems.
Generative Adversarial Networks (GANs) have been successfully used to
synthesize realistically looking medical images, however limited to low
resolution, whereas machine learning models for challenging tasks such as skin
lesion segmentation or classification benefit from much higher resolution data.
In this work, we successfully synthesize realistically looking images of skin
lesions with GANs at such high resolution. Therefore, we utilize the concept of
progressive growing, which we both quantitatively and qualitatively compare to
other GAN architectures such as the DCGAN and the LAPGAN. Our results show that
with the help of progressive growing, we can synthesize highly realistic
dermoscopic images of skin lesions that even expert dermatologists find hard to
distinguish from real ones.
