# Generating Highly Realistic Images of Skin Lesions with GANs

arXiv: https://arxiv.org/abs/1809.01410 · HTML: https://mlsanity.com/paper/1809.01410/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2018.

## Built on by (most cited first, 16 of 16)

- [Generative models improve fairness of medical classifiers under distribution shifts](https://mlsanity.com/paper/2304.09218/) (2023)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder](https://mlsanity.com/paper/2105.00026/) (2021)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation](https://mlsanity.com/paper/2302.02150/) (2023)
- [VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis](https://mlsanity.com/paper/2405.05667/) (2024)
- [DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images](https://mlsanity.com/paper/2305.12621/) (2023)
- [HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis](https://mlsanity.com/paper/2405.04902/) (2024)
- [CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning](https://mlsanity.com/paper/2406.05631/) (2024)
- [Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Sampling](https://mlsanity.com/paper/2412.12532/) (2024)
- [Multiscale Vector-Quantized Variational Autoencoder for Endoscopic Image Synthesis](https://mlsanity.com/paper/2511.19578/) (2025)
- [Synthetic Generation of Dermatoscopic Images with GAN and Closed-Form Factorization](https://mlsanity.com/paper/2410.05114/) (2024)
- [Decoupling Shape and Density for Liver Lesion Synthesis Using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2106.00629/) (2021)
- [Generative Adversarial Networks for anonymous Acneic face dataset generation](https://mlsanity.com/paper/2211.04214/) (2022)
- [The State of Applying Artificial Intelligence to Tissue Imaging for Cancer Research and Early Detection](https://mlsanity.com/paper/2306.16989/) (2023)
- [Method and Software Tool for Generating Artificial Databases of Biomedical Images Based on Deep Neural Networks](https://mlsanity.com/paper/2405.16119/) (2024)

## Abstract (arXiv)

As many other machine learning driven medical image analysis tasks, skin image analysis suffers from a chronic lack of labeled data and skewed class distributions, which poses problems for the training of robust and well-generalizing models. The ability to synthesize realistic looking images of skin lesions could act as a reliever for the aforementioned problems. Generative Adversarial Networks (GANs) have been successfully used to synthesize realistically looking medical images, however limited to low resolution, whereas machine learning models for challenging tasks such as skin lesion segmentation or classification benefit from much higher resolution data. In this work, we successfully synthesize realistically looking images of skin lesions with GANs at such high resolution. Therefore, we utilize the concept of progressive growing, which we both quantitatively and qualitatively compare to other GAN architectures such as the DCGAN and the LAPGAN. Our results show that with the help of progressive growing, we can synthesize highly realistic dermoscopic images of skin lesions that even expert dermatologists find hard to distinguish from real ones.