Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
It addresses the gap between advanced deep learning and neurology research to support clinical decision-making, but is incremental as it reviews existing literature.
This review examines the use of generative adversarial networks (GANs) in neuroimaging for conditions like Alzheimer's disease and brain tumors, highlighting their enhanced ability to capture complex disease effects compared to traditional methods.
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.