Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease
复制标题
使用多模态堆叠深度多项式网络进行多模态神经影像特征学习以诊断阿尔茨海默病
DOI：
10.1109/jbhi.2017.2655720
复制
发表时间：
2018-01-01
影响因子：
7.7
通讯作者：
Ying, Shihui
中科院分区：
文献类型：
--
作者：
Shi, Jun;Zheng, Xiao;Ying, Shihui
阿尔茨海默病（AD）及其早期阶段的准确诊断，即，轻度认知功能障碍，对于及时治疗和可能延迟AD至关重要。多模态神经成像数据的融合，如磁共振成像（MRI）和正电子发射断层扫描（PET），已显示其对AD诊断的有效性。深度多项式网络（DPN）是最近提出的一种深度学习算法，它在大规模和小规模数据集上都表现良好。在这项研究中，多模态堆叠DPN（MM-SDPN）算法，其中MM-SDPN包括两个阶段的SDPN，提出了融合和学习的特征表示从多模态神经影像数据AD诊断。具体来说，首先使用两个SDPN分别学习MRI和PET的高级特征，然后将其馈送到另一个SDPN以融合多模态神经成像信息。提出的MM-SDPN算法应用于ADNI数据集进行二进制分类和多类分类任务。实验结果表明，MM-SDPN是上级优于国家的最先进的多模态特征学习为基础的AD诊断算法。
The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.