Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease

Abstract

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.

Sex Differences in Predicting Fluid Intelligence of Adolescent Brain from T1-Weighted MRIs

“…Discovering connections between cognitive traits and non-invasive biomedical imaging could prove to be important for further understanding the neural underpinnings of cognitive development. Recently deep learning models trained on brain MRI data have shown promising results in the diagnosis of Alzheimer's disease [10], prediction of age [11,12] and classification of overall survival in brain tumor patients [8]. In this work we compared the performance of deep learning techniques with that of classic machine learning techniques trained on hand-crafted features in the prediction of Gf from MRI-based features.…”

Research of Low-Rank Representation and Discriminant Correlation Analysis for Alzheimer’s Disease Diagnosis

“…[24,25] is an improved algorithm based on canonical correlation analysis (CCA). e existing feature fusion algorithm [12][13][14][15]26] uses the neural network or sparse representation to jointly represent multimodal data, leading to suppress the relationship between multimodal data. CCA (20)(21)(22) can effectively model the relationship between multimodal data, but it cannot deal with the redundant information in the data.…”

“…Using multimodal data to detect Alzheimer's disease has grown up to be a research hotspot. A double-layer polynomial network [12] method is proposed. Firstly, the first-layer polynomial network extracts the high-level semantic features of MRI and PET data, and the second-layer polynomial network is employed to multimodal data fusion.…”

An intelligent Alzheimer’s disease diagnosis method using unsupervised feature learning

“…By reviewing the literatures on diagnosis of Alzheimer's disease, it was concluded that brain data and images with a low sample size and high dimension are one of the most important challenges in this study, and new research can be carried out in this area [12][13][14][15]. Most of the recently used methods are deep learning methods, including deep sparse multi-task learning [16], stacked auto-encoder [17], sparse regression models [18], etc., each attempting to overcome the aforementioned challenges.…”