mlsanityData to 7 Oct 2026

Generative Adversarial Network in Medical Imaging: A Review

arXiv 1809.07294 ↗First posted 19 Sep 2018cs.CV cs.LGcited by 257

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Cited by 30 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Generative adversarial networks have gained a lot of attention in the computer vision community due to their capability of data generation without explicitly modelling the probability density function. The adversarial loss brought by the discriminator provides a clever way of incorporating unlabeled samples into training and imposing higher order consistency. This has proven to be useful in many cases, such as domain adaptation, data augmentation, and image-to-image translation. These properties have attracted researchers in the medical imaging community, and we have seen rapid adoption in many traditional and novel applications, such as image reconstruction, segmentation, detection, classification, and cross-modality synthesis. Based on our observations, this trend will continue and we therefore conducted a review of recent advances in medical imaging using the adversarial training scheme with the hope of benefiting researchers interested in this technique.

Key facts. Cited by 257 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2018.

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Citing papers per year: 2021: 63; 2022: 51; 2023: 49; 2024: 38; 2025: 33; 2026: 23.

257 papers.

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  1. 2021GAN Inversion: A Surveycited by 366
  2. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
  3. 2022Transformers in Medical Imaging: A Surveycited by 245
  4. 2021ResViT: Residual vision transformers for multi-modal medical image synthesiscited by 177
  5. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  6. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  7. 2021Deep Learning for Chest X-ray Analysis: A Surveycited by 84
  8. 2023Membership Inference Attacks against Diffusion Modelscited by 84
  9. 2023Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Studycited by 64
  10. 2021DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Datacited by 62
  11. 2022Will Large-scale Generative Models Corrupt Future Datasets?cited by 59
  12. 2022SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generationcited by 56
38 more
  1. 2024Endora: Video Generation Models as Endoscopy Simulatorscited by 53
  2. 2023Deep Learning Approaches for Data Augmentation in Medical Imaging: A Reviewcited by 48
  3. 2022Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesiscited by 41
  4. 2021An error analysis of generative adversarial networks for learning distributionscited by 35
  5. 2023BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeyscited by 29
  6. 2021Deep learning-based synthetic-CT generation in radiotherapy and PET: a reviewcited by 28
  7. 2021Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencodercited by 27
  8. 2022Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Modelscited by 27
  9. 2024Hallucination Index: An Image Quality Metric for Generative Reconstruction Modelscited by 25
  10. 2022A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysiscited by 24
  11. 2023Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Modelscited by 24
  12. 2023Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray imagescited by 23
  13. 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
  14. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  15. 2023Generated Distributions Are All You Need for Membership Inference Attacks Against Generative Modelscited by 22
  16. 2023Continual Learning in Medical Image Analysis: A Comprehensive Review of Recent Advancements and Future Prospectscited by 22
  17. 2021Medical Image Segmentation with Limited Supervision: A Review of Deep Network Modelscited by 21
  18. 2021Generative adversarial networks in time series: A survey and taxonomycited by 20
  19. 2022Deep Learning for Computational Cytology: A Surveycited by 20
  20. 2023Medical diffusion on a budget: Textual Inversion for medical image generationcited by 20
  21. 2024Similarity and Quality Metrics for MR Image-To-Image Translationcited by 20
  22. 2021On the capacity of deep generative networks for approximating distributionscited by 19
  23. 2023Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion modelscited by 19
  24. 2023Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesiscited by 19
  25. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  26. 2022Structure-preserving GANscited by 17
  27. 2021Uncertainty-Guided Progressive GANs for Medical Image Translationcited by 16
  28. 2021Medical Image Segmentation with 3D Convolutional Neural Networks: A Surveycited by 16
  29. 2021Mutually improved endoscopic image synthesis and landmark detection in unpaired image-to-image translationcited by 15
  30. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  31. 2023AugDMC: Data Augmentation Guided Deep Multiple Clusteringcited by 15
  32. 2021Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstructioncited by 14
  33. 2022ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseasescited by 14
  34. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
  35. 2024Generative Enhancement for 3D Medical Imagescited by 14
  36. 2021An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future workscited by 13
  37. 2022Masked Autoencoders are Robust Data Augmentorscited by 13
  38. 2022FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomycited by 13

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