# GANs for Medical Image Analysis

arXiv: https://arxiv.org/abs/1809.06222 · HTML: https://mlsanity.com/paper/1809.06222/

## Key facts

- Cited by 81 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 30 of 81)

- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Diffusion Models for Medical Image Analysis: A Comprehensive Survey](https://mlsanity.com/paper/2211.07804/) (2022)
- [Recent advances and clinical applications of deep learning in medical image analysis](https://mlsanity.com/paper/2105.13381/) (2021)
- [Conditional Diffusion Models for Semantic 3D Brain MRI Synthesis](https://mlsanity.com/paper/2305.18453/) (2023)
- [GANs for Medical Image Synthesis: An Empirical Study](https://mlsanity.com/paper/2105.05318/) (2021)
- [A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis](https://mlsanity.com/paper/2201.07646/) (2022)
- [Learning Stochastic Dynamical System via Flow Map Operator](https://mlsanity.com/paper/2305.03874/) (2023)
- [A Survey on Deep Learning for Skin Lesion Segmentation](https://mlsanity.com/paper/2206.00356/) (2022)
- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021)
- [Adversarial Continual Learning for Multi-Domain Hippocampal Segmentation](https://mlsanity.com/paper/2107.08751/) (2021)
- [FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy](https://mlsanity.com/paper/2207.01390/) (2022)
- [Unsupervised Model Adaptation for Source-free Segmentation of Medical Images](https://mlsanity.com/paper/2211.00807/) (2022)
- [VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis](https://mlsanity.com/paper/2307.03592/) (2023)
- [Domain Adaptation for the Segmentation of Confidential Medical Images](https://mlsanity.com/paper/2101.00522/) (2021)
- [Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2401.13699/) (2024)
- [METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopy](https://mlsanity.com/paper/2104.10993/) (2021)
- [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)
- [Regression is all you need for medical image translation](https://mlsanity.com/paper/2505.02048/) (2025)
- [Contrastive Counterfactual Visual Explanations With Overdetermination](https://mlsanity.com/paper/2106.14556/) (2021)
- [Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANs](https://mlsanity.com/paper/2209.04223/) (2022)
- [Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion](https://mlsanity.com/paper/2303.02094/) (2023)
- [Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context](https://mlsanity.com/paper/2309.10817/) (2023)
- [Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey](https://mlsanity.com/paper/2209.11123/) (2022)
- [Diffusion map particle systems for generative modeling](https://mlsanity.com/paper/2304.00200/) (2023)
- [Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splatting](https://mlsanity.com/paper/2407.14846/) (2024)
- [Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease](https://mlsanity.com/paper/2107.12775/) (2021)
- [Towards Realistic Ultrasound Fetal Brain Imaging Synthesis](https://mlsanity.com/paper/2304.03941/) (2023)
- [CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets](https://mlsanity.com/paper/2307.13842/) (2023)

## Abstract (arXiv)

Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed and potential future work is elaborated. We review the most relevant papers published until the submission date. For quick access, important details such as the underlying method, datasets and performance are tabulated. An interactive visualization which categorizes all papers to keep the review alive, is available at http://livingreview.in.tum.de/GANs_for_Medical_Applications.