mlsanityData to 7 Oct 2026

Synthetic Medical Images from Dual Generative Adversarial Networks

arXiv 1709.01872 ↗First posted 6 Sep 2017cs.CVcited by 32

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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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Sep 2017.

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Citing papers per year: 2021: 6; 2022: 4; 2023: 11; 2024: 7; 2025: 1; 2026: 3.

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  1. 2024Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generationcited by 129
  2. 2021Ensembling with Deep Generative Viewscited by 53
  3. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  4. 2023MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generationcited by 34
  5. 2024Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Modelscited by 22
  6. 2021Generative adversarial networks in time series: A survey and taxonomycited by 20
  7. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelscited by 19
  8. 2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
  9. 2024PairAug: What Can Augmented Image-Text Pairs Do for Radiology?cited by 10
  10. 2021Controllable cardiac synthesis via disentangled anatomy arithmeticcited by 9
  11. 2023Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusioncited by 7
  12. 2021Ensembles of GANs for synthetic training data generationcited by 6
20 more
  1. 2023DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelscited by 6
  2. 2023Between Generating Noise and Generating Images: Noise in the Correct Frequency Improves the Quality of Synthetic Histopathology Images for Digital Pathologycited by 5
  3. 2023Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentationcited by 4
  4. 2022Deep learning based domain adaptation for mitochondria segmentation on EM volumescited by 2
  5. 2022ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Datacited by 2
  6. 2022Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?cited by 2
  7. 2023DEPAS: De-novo Pathology Semantic Masks using a Generative Modelcited by 2
  8. 2023End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation maskscited by 2
  9. 2024Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentationcited by 2
  10. 2024CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generationcited by 2
  11. 2025FundusGAN: A Hierarchical Feature-Aware Generative Framework for High-Fidelity Fundus Image Generationcited by 2
  12. 2023Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Datacited by 1
  13. 2024PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generationcited by 1
  14. 2021Synthetic Data and Hierarchical Object Detection in Overhead Imagerycited by 0
  15. 2021TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priorscited by 0
  16. 2022Deep Learning-Based MR Image Re-parameterizationcited by 0
  17. 2024Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathycited by 0
  18. 2026Sketch2CT: Multimodal Diffusion for Structure-Aware 3D Medical Volume Generationcited by 0
  19. 2026Limitations of Synthetic Data Generation in Specialized Data-Scarce Domainscited by 0
  20. 2026To do(x) or not to do(x): Medical Image Counterfactuals for Dataset Augmentationcited by 0