Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience. (arXiv:2206.07081v1 [cs.LG])

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 …

applications arxiv generative adversarial networks lg networks neuroimaging neuroscience