论文标题
基于神经影像学的大脑疾病分析的深度学习调查
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
论文作者
论文摘要
最近，深度学习已用于分析神经图像，例如结构磁共振成像（MRI），功能性MRI和正电子发射断层扫描（PET），并在计算机辅助诊断脑疾病的计算机辅助诊断中实现了显着的性能。本文回顾了深度学习方法对基于神经影像学的脑疾病分析的应用。我们首先通过引入各种类型的深神经网络和最新发展，对深度学习技术和流行网络体系结构进行全面概述。然后，我们回顾了四种典型脑疾病的计算机辅助分析的深度学习方法，包括阿尔茨海默氏病，帕金森氏病，自闭症谱系障碍和精神分裂症，那里的前两种疾病是神经退行性疾病，最后两种是神经产生和精神疾病。更重要的是，我们讨论现有研究的局限性，并提出可能的未来方向。
Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures, by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.
