# Skin Lesion Synthesis with Generative Adversarial Networks

## TL;DR

This paper introduces a novel use of Generative Adversarial Networks to create realistic synthetic skin lesion images, aiming to address data scarcity in skin cancer classification.

## Contribution

It is the first to generate visually appealing, clinically meaningful synthetic skin lesion images using GANs for medical imaging applications.

## Key findings

- Generated images are visually realistic and clinically meaningful.
- Synthetic data can potentially augment training datasets for skin cancer detection.
- First demonstration of GANs producing clinically relevant skin lesion images.

## Abstract

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.