# DermGAN: Synthetic Generation of Clinical Skin Images with Pathology

arXiv: https://arxiv.org/abs/1911.08716 · HTML: https://mlsanity.com/paper/1911.08716/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## Built on by (most cited first, 30 of 33)

- [A Petri Dish for Histopathology Image Analysis](https://mlsanity.com/paper/2101.12355/) (2021)
- [GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review](https://mlsanity.com/paper/2104.10603/) (2021)
- [Improving dermatology classifiers across populations using images generated by large diffusion models](https://mlsanity.com/paper/2211.13352/) (2022)
- [Augmenting medical image classifiers with synthetic data from latent diffusion models](https://mlsanity.com/paper/2308.12453/) (2023)
- [Similarity and Quality Metrics for MR Image-To-Image Translation](https://mlsanity.com/paper/2405.08431/) (2024)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations](https://mlsanity.com/paper/2201.08186/) (2022)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Forensic Analysis of Synthetically Generated Western Blot Images](https://mlsanity.com/paper/2112.08739/) (2021)
- [Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation](https://mlsanity.com/paper/2307.03157/) (2023)
- [From Majority to Minority: A Diffusion-based Augmentation for Underrepresented Groups in Skin Lesion Analysis](https://mlsanity.com/paper/2406.18375/) (2024)
- [Synthetic Data in Healthcare](https://mlsanity.com/paper/2304.03243/) (2023)
- [Self-Supervised Multi-Modal Alignment for Whole Body Medical Imaging](https://mlsanity.com/paper/2107.06652/) (2021)
- [S-SYNTH: Knowledge-Based, Synthetic Generation of Skin Images](https://mlsanity.com/paper/2408.00191/) (2024)
- [Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images](https://mlsanity.com/paper/2109.11480/) (2021)
- [Synthetic Medical Imaging Generation with Generative Adversarial Networks For Plain Radiographs](https://mlsanity.com/paper/2403.19107/) (2024)
- [eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases](https://mlsanity.com/paper/2508.18608/) (2025)
- [Generation of microbial colonies dataset with deep learning style transfer](https://mlsanity.com/paper/2111.03789/) (2021)
- [Knowledge-driven AI-generated data for accurate and interpretable breast ultrasound diagnoses](https://mlsanity.com/paper/2407.16634/) (2024)
- [Conditional Synthetic Data Generation for Personal Thermal Comfort Models](https://mlsanity.com/paper/2203.05242/) (2022)
- [Deepfake histological images for enhancing digital pathology](https://mlsanity.com/paper/2206.08308/) (2022)
- [High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data](https://mlsanity.com/paper/2303.04839/) (2023)
- [Cancer-Net SCa-Synth: An Open Access Synthetically Generated 2D Skin Lesion Dataset for Skin Cancer Classification](https://mlsanity.com/paper/2411.05269/) (2024)
- [A Model-Based Approach to Synthetic Data Set Generation for Patient-Ventilator Waveforms for Machine Learning and Educational Use](https://mlsanity.com/paper/2103.15684/) (2021)
- [SYNTA: A novel approach for deep learning-based image analysis in muscle histopathology using photo-realistic synthetic data](https://mlsanity.com/paper/2207.14650/) (2022)
- [Generative Adversarial Networks for anonymous Acneic face dataset generation](https://mlsanity.com/paper/2211.04214/) (2022)
- [Automated Measurement of Eczema Severity with Self-Supervised Learning](https://mlsanity.com/paper/2504.15193/) (2025)
- [Towards Facilitated Fairness Assessment of AI-based Skin Lesion Classifiers Through GenAI-based Image Synthesis](https://mlsanity.com/paper/2507.17860/) (2025)
- [Automated Disentangling Analysis of Skin Colour for Lesion Images](https://mlsanity.com/paper/2602.19055/) (2026)

## Abstract (arXiv)

Despite the recent success in applying supervised deep learning to medical imaging tasks, the problem of obtaining large and diverse expert-annotated datasets required for the development of high performant models remains particularly challenging. In this work, we explore the possibility of using Generative Adverserial Networks (GAN) to synthesize clinical images with skin condition. We propose DermGAN, an adaptation of the popular Pix2Pix architecture, to create synthetic images for a pre-specified skin condition while being able to vary its size, location and the underlying skin color. We demonstrate that the generated images are of high fidelity using objective GAN evaluation metrics. In a Human Turing test, we note that the synthetic images are not only visually similar to real images, but also embody the respective skin condition in dermatologists' eyes. Finally, when using the synthetic images as a data augmentation technique for training a skin condition classifier, we observe that the model performs comparably to the baseline model overall while improving on rare but malignant conditions.