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.
翻译:产生对抗性网络(GANs)是在许多领域成功使用的一种强有力的深层次学习模式,属于一个称为基因方法的大家庭,它通过从实际实例中学习样本分布,产生具有概率模型的新数据;在临床方面,GANs显示在捕捉空间复杂、非线性以及潜在的隐蔽疾病影响方面的能力,与传统基因方法相比,这种能力得到加强;本审查评估了关于GANs在包括阿尔茨海默氏病、脑肿瘤、大脑老化和多重硬化在内的各种神经条件成像研究中的应用的现有文献;我们为每一种应用提供了各种GAN方法的直观解释,并进一步讨论了利用GANs进行神经成型的主要挑战、开放问题和有希望的未来方向;我们的目的是缩小先进的深层学习方法和神经学研究之间的差距,强调如何利用GANs支持临床决策,并促进更好地了解脑疾病的结构和功能模式。