# Skin Lesion Synthesis with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1902.03253 · HTML: https://mlsanity.com/paper/1902.03253/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 22 of 22)

- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Soft-Attention Improves Skin Cancer Classification Performance](https://mlsanity.com/paper/2105.03358/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Private Synthetic Text Generation with Diffusion Models](https://mlsanity.com/paper/2410.22971/) (2024)
- [LesionMix: A Lesion-Level Data Augmentation Method for Medical Image Segmentation](https://mlsanity.com/paper/2308.09026/) (2023)
- [Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets](https://mlsanity.com/paper/2204.02275/) (2022)
- [Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images](https://mlsanity.com/paper/2208.02135/) (2022)
- [Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathology](https://mlsanity.com/paper/2302.06549/) (2023)
- [DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images](https://mlsanity.com/paper/2305.12621/) (2023)
- [Content-Aware Differential Privacy with Conditional Invertible Neural Networks](https://mlsanity.com/paper/2207.14625/) (2022)
- [Knowledge Distillation approach towards Melanoma Detection](https://mlsanity.com/paper/2210.08086/) (2022)
- [Generative Adversarial Networks based Skin Lesion Segmentation](https://mlsanity.com/paper/2305.18164/) (2023)
- [eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases](https://mlsanity.com/paper/2508.18608/) (2025)
- [Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans](https://mlsanity.com/paper/2106.04281/) (2021)
- [Generation of microbial colonies dataset with deep learning style transfer](https://mlsanity.com/paper/2111.03789/) (2021)
- [High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data](https://mlsanity.com/paper/2303.04839/) (2023)
- [Segmentation of skin lesions and their attributes using Generative Adversarial Networks](https://mlsanity.com/paper/2102.00169/) (2021)
- [GAN-based generative modelling for dermatological applications -- comparative study](https://mlsanity.com/paper/2208.11702/) (2022)
- [Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis](https://mlsanity.com/paper/2507.17860/) (2025)
- [Synthetic Melanoma Image Generation and Evaluation Using Generative Adversarial Networks](https://mlsanity.com/paper/2603.13497/) (2026)
- [CAMEO: A Class-Activation-Mapped Equitable Overlay Framework for Fair and Robust Deep Learning-based Skin Condition Diagnosis](https://mlsanity.com/paper/2609.36400/) (2026)

## Abstract (arXiv)

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.