3 research outputs found
Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
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