mlsanityData to 14 Oct 2026

Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience

arXiv 2206.07081 ↗First posted 14 Jun 2022cs.LG cs.CV eess.IVcited by 11

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Abstract (arXiv)

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which generate new data with a probabilistic model by learning sample distribution from real examples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 38 of its 174 references that are in the map. First posted on arXiv 14 Jun 2022.

Builds on

38 of 174 references.

References · 38

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26 more
  1. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 511
  3. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  4. 2020A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promisescited by 231
  5. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
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  7. 2018Image Synthesis in Multi-Contrast MRI with Conditional Generative Adversarial Networkscited by 132
  8. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
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  10. 20203D Deep Learning on Medical Images: A Reviewcited by 76
  11. 2018ClusterGAN : Latent Space Clustering in Generative Adversarial Networkscited by 75
  12. 2019Learning to synthesise the ageing brain without longitudinal datacited by 42
  13. 2020Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesiscited by 38
  14. 2019Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementiacited by 34
  15. 2019Scanner Invariant Representations for Diffusion MRI Harmonizationcited by 27
  16. 2019Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detectioncited by 24
  17. 2021NeuroGen: activation optimized image synthesis for discovery neurosciencecited by 22
  18. 2019Degenerative Adversarial NeuroImage Nets: Generating Images that Mimic Disease Progressioncited by 21
  19. 2020Tensorizing GAN with High-Order Pooling for Alzheimer's Disease Assessmentcited by 21
  20. 2019Reversible GANs for Memory-efficient Image-to-Image Translationcited by 19
  21. 2021Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptationcited by 19
  22. 2020Unsupervised Region-based Anomaly Detection in Brain MRI with Adversarial Image Inpaintingcited by 15
  23. 2021Harmonization with Flow-based Causal Inferencecited by 14
  24. 2021DecGAN: Decoupling Generative Adversarial Network detecting abnormal neural circuits for Alzheimer's diseasecited by 11
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  26. 2022Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patternscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 4; 2026: 2.

11 papers.

  1. 2024Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PETcited by 10
  2. 2023Deep Generative Models for Physiological Signals: A Systematic Literature Reviewcited by 7
  3. 2025From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPTcited by 4
  4. 2025Generative Learning of Densities on Manifoldscited by 4
  5. 2023Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neurosciencecited by 2
  6. 2024Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methodscited by 1
  7. 2024Generative models of MRI-derived neuroimaging features and associated dataset of 18,000 samplescited by 1
  8. 2025Unsupervised Deep Generative Models for Anomaly Detection in Neuroimaging: A Systematic Scoping Reviewcited by 1
  9. 2025Enabling Probabilistic Learning on Manifolds through Double Diffusion Mapscited by 0
  10. 2026Latent graph encoding of multimodal neuroimaging features with generative AI architecturescited by 0
  11. 2026Data-driven techniques for translational neuroscience and personalized neuro-healthcited by 0