mlsanityData to 7 Oct 2026

GANs for Medical Image Analysis

arXiv 1809.06222 ↗First posted 13 Sep 2018cs.CV cs.LG stat.MLcited by 81

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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.

Key facts. Cited by 81 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Sep 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 14; 2023: 18; 2024: 12; 2025: 14; 2026: 6.

81 papers.

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  1. 2023Extracting Training Data from Diffusion Modelscited by 723
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  3. 2021Recent advances and clinical applications of deep learning in medical image analysiscited by 104
  4. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 85
  5. 2021GANs for Medical Image Synthesis: An Empirical Studycited by 64
  6. 2022A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysiscited by 24
  7. 2023Learning Stochastic Dynamical System via Flow Map Operatorcited by 24
  8. 2022A Survey on Deep Learning for Skin Lesion Segmentationcited by 22
  9. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  10. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  11. 2021Adversarial Continual Learning for Multi-Domain Hippocampal Segmentationcited by 14
  12. 2022FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomycited by 13
38 more
  1. 2022Unsupervised Model Adaptation for Source-free Segmentation of Medical Imagescited by 13
  2. 2023VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesiscited by 13
  3. 2021Domain Adaptation for the Segmentation of Confidential Medical Imagescited by 12
  4. 2024Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Surveycited by 11
  5. 2021METGAN: Generative Tumour Inpainting and Modality Synthesis in Light Sheet Microscopycited by 9
  6. 2023This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generationcited by 9
  7. 2024VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesiscited by 8
  8. 2025Regression is all you need for medical image translationcited by 8
  9. 2021Contrastive Counterfactual Visual Explanations With Overdeterminationcited by 7
  10. 2022Pathology Synthesis of 3D-Consistent Cardiac MR Images using 2D VAEs and GANscited by 7
  11. 2023Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusioncited by 7
  12. 2023Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial contextcited by 7
  13. 2022Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Surveycited by 6
  14. 2023Diffusion map particle systems for generative modelingcited by 6
  15. 2024Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splattingcited by 6
  16. 2021Realistic Ultrasound Image Synthesis for Improved Classification of Liver Diseasecited by 5
  17. 2023Towards Realistic Ultrasound Fetal Brain Imaging Synthesiscited by 5
  18. 2023CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasetscited by 5
  19. 2023Medical Image Segmentation with Domain Adaptation: A Surveycited by 5
  20. 2022Interpreting Latent Spaces of Generative Models for Medical Images using Unsupervised Methodscited by 4
  21. 20223D Brain and Heart Volume Generative Models: A Surveycited by 4
  22. 2023DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Imagescited by 4
  23. 2024NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimationcited by 4
  24. 2024Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statisticscited by 4
  25. 2021Improving Tuberculosis (TB) Prediction using Synthetically Generated Computed Tomography (CT) Imagescited by 3
  26. 2022An Overview on the Generation and Detection of Synthetic and Manipulated Satellite Imagescited by 3
  27. 2023Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalancecited by 3
  28. 2024Evaluating Synthetic Data Generation for Domain Generalization in Fetal Brain MRI Segmentationcited by 3
  29. 2025Diffusion Models for conditional MRI generationcited by 3
  30. 2025Diffusion-Based Data Augmentation for Medical Image Segmentationcited by 3
  31. 2025Systematic Review and Meta-analysis of AI-driven MRI Motion Artifact Detection and Correctioncited by 3
  32. 2026NeoNet: An End-to-End 3D MRI-Based Deep Learning Framework for Non-Invasive Prediction of Perineural Invasion via Generation-Driven Classificationcited by 3
  33. 2021MITNet: GAN Enhanced Magnetic Induction Tomography Based on Complex CNNcited by 2
  34. 2021Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networkscited by 2
  35. 2021A Method for Evaluating Deep Generative Models of Images via Assessing the Reproduction of High-order Spatial Contextcited by 2
  36. 2023Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networkscited by 2
  37. 2024Addressing Small and Imbalanced Medical Image Datasets Using Generative Models: A Comparative Study of DDPM and PGGANs with Random and Greedy K Samplingcited by 2
  38. 2021A Deep Learning Approach to Private Data Sharing of Medical Images Using Conditional GANscited by 1

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