# GAN-Based Data Augmentation and Anonymization for Skin-Lesion Analysis: A Critical Review

arXiv: https://arxiv.org/abs/2104.10603 · HTML: https://mlsanity.com/paper/2104.10603/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 49 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 30 of 30)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification](https://mlsanity.com/paper/1803.01229/) (2018)
- [Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks](https://mlsanity.com/paper/1807.10225/) (2018)
- [A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context](https://mlsanity.com/paper/2008.07360/) (2020)
- [Debiasing Skin Lesion Datasets and Models? Not So Fast](https://mlsanity.com/paper/2004.11457/) (2020)
- [DermGAN: Synthetic Generation of Clinical Skin Images with Pathology](https://mlsanity.com/paper/1911.08716/) (2019)
- [(De)Constructing Bias on Skin Lesion Datasets](https://mlsanity.com/paper/1904.08818/) (2019)
- [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020)
- [Data Augmentation for Skin Lesion Analysis](https://mlsanity.com/paper/1809.01442/) (2018)
- [Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection](https://mlsanity.com/paper/1905.13456/) (2019)
- [Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies](https://mlsanity.com/paper/2012.13736/) (2020)
- [Skin Lesion Synthesis with Generative Adversarial Networks](https://mlsanity.com/paper/1902.03253/) (2019)
- [Knowledge Transfer for Melanoma Screening with Deep Learning](https://mlsanity.com/paper/1703.07479/) (2017)
- [Conditional Infilling GANs for Data Augmentation in Mammogram Classification](https://mlsanity.com/paper/1807.08093/) (2018)
- [Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation](https://mlsanity.com/paper/2003.14119/) (2020)
- [Synthetic Sample Selection via Reinforcement Learning](https://mlsanity.com/paper/2008.11331/) (2020)
- [Red blood cell image generation for data augmentation using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/1901.06219/) (2019)
- [XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms](https://mlsanity.com/paper/2007.13408/) (2020)
- [Informative sample generation using class aware generative adversarial networks for classification of chest Xrays](https://mlsanity.com/paper/1904.10781/) (2019)
- [Data, Depth, and Design: Learning Reliable Models for Skin Lesion Analysis](https://mlsanity.com/paper/1711.00441/) (2017)

## Built on by (most cited first, 25 of 25)

- [Generative models improve fairness of medical classifiers under distribution shifts](https://mlsanity.com/paper/2304.09218/) (2023)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [AI-Generated Images as Data Source: The Dawn of Synthetic Era](https://mlsanity.com/paper/2310.01830/) (2023)
- [An Optimal Transport Perspective on Unpaired Image Super-Resolution](https://mlsanity.com/paper/2202.01116/) (2022)
- [The (de)biasing effect of GAN-based augmentation methods on skin lesion images](https://mlsanity.com/paper/2206.15182/) (2022)
- [Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation](https://mlsanity.com/paper/2307.03157/) (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)
- [Data efficient deep learning for medical image analysis: A survey](https://mlsanity.com/paper/2310.06557/) (2023)
- [GenFormer -- Generated Images are All You Need to Improve Robustness of Transformers on Small Datasets](https://mlsanity.com/paper/2408.14131/) (2024)
- [Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging](https://mlsanity.com/paper/2205.07085/) (2022)
- [Generative Adversarial Networks for Image Augmentation in Agriculture: A Systematic Review](https://mlsanity.com/paper/2204.04707/) (2022)
- [SafeTriage: Facial Video De-identification for Privacy-Preserving Stroke Triage](https://mlsanity.com/paper/2506.16578/) (2025)
- [eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases](https://mlsanity.com/paper/2508.18608/) (2025)
- [Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models](https://mlsanity.com/paper/2406.06372/) (2024)
- [Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning](https://mlsanity.com/paper/2412.03252/) (2024)
- [A Robust Framework of Chromosome Straightening with ViT-Patch GAN](https://mlsanity.com/paper/2203.02901/) (2022)
- [High Fidelity Synthetic Face Generation for Rosacea Skin Condition from Limited Data](https://mlsanity.com/paper/2303.04839/) (2023)
- [General Purpose Artificial Intelligence Systems (GPAIS): Properties, Definition, Taxonomy, Societal Implications and Responsible Governance](https://mlsanity.com/paper/2307.14283/) (2023)
- [WasteGAN: Data Augmentation for Robotic Waste Sorting through Generative Adversarial Networks](https://mlsanity.com/paper/2409.16999/) (2024)
- [Local Lesion Generation is Effective for Capsule Endoscopy Image Data Augmentation in a Limited Data Setting](https://mlsanity.com/paper/2411.03098/) (2024)
- [Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) Assessment](https://mlsanity.com/paper/2501.17690/) (2025)
- [GAN-based generative modelling for dermatological applications -- comparative study](https://mlsanity.com/paper/2208.11702/) (2022)
- [Optimizing Deep Learning for Skin Cancer Classification: A Computationally Efficient CNN with Minimal Accuracy Trade-Off](https://mlsanity.com/paper/2505.21597/) (2025)
- [AURAD: Anatomy-Pathology Unified Radiology Synthesis with Progressive Representations](https://mlsanity.com/paper/2509.04819/) (2025)
- [Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2607.06484/) (2026)

## Abstract (arXiv)

Despite the growing availability of high-quality public datasets, the lack of training samples is still one of the main challenges of deep-learning for skin lesion analysis. Generative Adversarial Networks (GANs) appear as an enticing alternative to alleviate the issue, by synthesizing samples indistinguishable from real images, with a plethora of works employing them for medical applications. Nevertheless, carefully designed experiments for skin-lesion diagnosis with GAN-based data augmentation show favorable results only on out-of-distribution test sets. For GAN-based data anonymization - where the synthetic images replace the real ones - favorable results also only appear for out-of-distribution test sets. Because of the costs and risks associated with GAN usage, those results suggest caution in their adoption for medical applications.