MelanoGANs: High Resolution Skin Lesion Synthesis with GANs

Understand

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.