IEEE J Biomed Health Inform. 2018 Jan;22(1):173-183. doi: 10.1109/JBHI.2017.2655720. Epub 2017 Jan 19.
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.
阿尔茨海默病（AD）的准确诊断及其早期阶段，即轻度认知障碍，对于及时治疗和可能延缓 AD 非常重要。融合磁共振成像（MRI）和正电子发射断层扫描（PET）等多模态神经影像学数据已显示出对 AD 诊断的有效性。深度多项式网络（DPN）是一种最近提出的深度学习算法，它在大规模和小数据集上都表现出色。在这项研究中，提出了一种多模态堆叠 DPN（MM-SDPN）算法，该算法由两阶段 SDPN 组成，用于融合和学习多模态神经影像学数据的特征表示，以进行 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，然后将这些特征输入另一个 SDPN 以融合多模态神经影像学信息。将所提出的 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，与基于先进的多模态特征学习的算法相比，MM-SDPN 更适合 AD 诊断。
