MelanoGANs: High Resolution Skin Lesion Synthesis with GANs

TL;DR

This paper demonstrates the ability of GANs to generate high-resolution skin lesion images from a small dataset, improving data diversity and aiding in class imbalance for melanoma classification.

Contribution

It introduces a modified LAPGAN architecture for high-resolution skin lesion synthesis using limited data, and evaluates its effectiveness compared to other GANs.

Findings

All models approximate real data distribution.

Visual differences in realism, diversity, artifacts.

Synthetic images help address class imbalance in melanoma classification.

Abstract

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.