# Synthetic Medical Images from Dual Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1709.01872 · HTML: https://mlsanity.com/paper/1709.01872/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2017.

## Built on by (most cited first, 30 of 32)

- [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://mlsanity.com/paper/2406.11824/) (2024)
- [Ensembling with Deep Generative Views](https://mlsanity.com/paper/2104.14551/) (2021)
- [Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review](https://mlsanity.com/paper/2307.13125/) (2023)
- [MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation](https://mlsanity.com/paper/2304.04106/) (2023)
- [Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models](https://mlsanity.com/paper/2406.18580/) (2024)
- [Generative adversarial networks in time series: A survey and taxonomy](https://mlsanity.com/paper/2107.11098/) (2021)
- [Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models](https://mlsanity.com/paper/2306.02986/) (2023)
- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [PairAug: What Can Augmented Image-Text Pairs Do for Radiology?](https://mlsanity.com/paper/2404.04960/) (2024)
- [Controllable cardiac synthesis via disentangled anatomy arithmetic](https://mlsanity.com/paper/2107.01748/) (2021)
- [Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion](https://mlsanity.com/paper/2303.02094/) (2023)
- [Ensembles of GANs for synthetic training data generation](https://mlsanity.com/paper/2104.11797/) (2021)
- [DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion models](https://mlsanity.com/paper/2304.13416/) (2023)
- [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)
- [Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation](https://mlsanity.com/paper/2308.08339/) (2023)
- [Deep learning based domain adaptation for mitochondria segmentation on EM volumes](https://mlsanity.com/paper/2202.10773/) (2022)
- [ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data](https://mlsanity.com/paper/2210.15137/) (2022)
- [Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?](https://mlsanity.com/paper/2211.04086/) (2022)
- [DEPAS: De-novo Pathology Semantic Masks using a Generative Model](https://mlsanity.com/paper/2302.06513/) (2023)
- [End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks](https://mlsanity.com/paper/2311.10472/) (2023)
- [Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation](https://mlsanity.com/paper/2404.06240/) (2024)
- [CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation](https://mlsanity.com/paper/2412.05833/) (2024)
- [FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generation](https://mlsanity.com/paper/2503.17831/) (2025)
- [Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data](https://mlsanity.com/paper/2307.12033/) (2023)
- [PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation](https://mlsanity.com/paper/2411.16515/) (2024)
- [Synthetic Data and Hierarchical Object Detection in Overhead Imagery](https://mlsanity.com/paper/2102.00103/) (2021)
- [TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors](https://mlsanity.com/paper/2102.02690/) (2021)
- [Deep Learning-Based MR Image Re-parameterization](https://mlsanity.com/paper/2206.05516/) (2022)
- [Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy](https://mlsanity.com/paper/2409.07422/) (2024)
- [Sketch2CT: Multimodal Diffusion for Structure-Aware 3D Medical Volume Generation](https://mlsanity.com/paper/2603.22509/) (2026)

## Abstract (arXiv)

Currently there is strong interest in data-driven approaches to medical image classification. However, medical imaging data is scarce, expensive, and fraught with legal concerns regarding patient privacy. Typical consent forms only allow for patient data to be used in medical journals or education, meaning the majority of medical data is inaccessible for general public research. We propose a novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images. We develop a hierarchical generation process to divide the complex image generation task into two parts: geometry and photorealism. We hope researchers will use our pipeline to bring private medical data into the public domain, sparking growth in imaging tasks that have previously relied on the hand-tuning of models. We have begun this initiative through the development of SynthMed, an online repository for synthetic medical images.