Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience

• URL: http://arxiv.org/abs/2206.07081v1
• Date: Tue, 14 Jun 2022 18:10:00 GMT
• Title: Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience
• Authors: Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Lasya P. Sreepada, Sai Spandana Chintapalli, Dushyant Sahoo, Ioanna Skampardoni, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen, Christos Davatzikos
• Abstract summary: Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects. This review appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions.
• Score: 4.394368629380544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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