Multi-Modal Neuroimaging Feature Learning for Multi-Class Diagnosis of Alzheimer’s Disease

By Siqi Liu, Sidong Liu, Weidong (Tom) Cai, Hangyu Che, Sonia Pujol, Ron Kikinis, D. Feng, Michael J. Fulham. IEEE Transactions on Biomedical Engineering, 2014

524 citations recorded by the source corpus.

The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.

Subfields: Artificial Intelligence, Molecular Biology, Psychiatry and Mental health

View Paper: https://www.semanticscholar.org/paper/101aad03db53cac05e89e5d474fe68948afae09d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

In Psychiatry and Mental health

Citations in the corpus

Quo vadis artificial intelligence? (2022, background): “For instance, AI helps to markup suspicious lesion areas on optical images [106], X-ray images [107], or magnetic resonance images [108–110], and can analyze patients’ speech to diagnose Alzheimer’s disease [111].”
DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images (2021, unknown): “For initiation of clinical progress and efﬁcient patient treatment, early and precise analysis of AD prediction is necessary [1].”
Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment (2018, background): “…classify AD patients at diﬀerent disease stages or to predict the MCI-to-AD conversion (Cuingnet et al., 2011; Zhang et al., 2011; Tong et al., 2013, 2017; Guerrero et al., 2014; Suk et al., 2014; Cheng et al., 2015; Eskildsen et al., 2015; Li et al., 2015; Liu et al., 2015; Moradi et al., 2015).”
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, methodology): “Liu et al. [76] have developed a multimodal stacked autoen-coder network using zero-masking strategy.”
Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “However, recent advancements in IS are permeating almost every aspect of our life with the introduction of intelligent transportation [1-10], intelligent diagnosis and health monitoring for precision medicine [11-14], robotics and automation in home appliances [15], virtual online assistance [16], e”
Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images (2017, unknown): “…into cortical and subcortical gray-matter compartments, and further subdividing each into patches of a hierarchical size, and extract features from each-sized patch 26–28 by averaging within the patch and use these multi-scale features taken from multiple modalities into a deep learning framework.”
Alzheimer's disease diagnostics by adaptation of 3D convolutional network (2016, methodology): “[13] extracted 83 regions-of-interest (ROI) from the MRI and PET scans and used multimodal fusion to create a set of features to train stacked layers of denoising autoencoders.”
Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI (2018, methodology): “Our proposed H-FCN method is also different from existing deep-learning-based AD diagnosis methods in the literature [31], [33], [34], [50], [51], [66], [68].”
Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease (2018, methodology): “While in our MM-SDPN algorithm, since DPN performs the feedforward supervised learning in each network layer without fine-turning, it is difficult to conduct the same learning strategy as in [38] to infer the correlations between MRI and PET.”
Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer’s Disease Diagnosis (2018, methodology): “Recently, deep learning networks were also used to extract the latent features from measurements of ROIs with different image modalities for AD classification (Liu et al. 2015; Suk et al. 2015).”
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks (2019, methodology): “Siqi Liu et al. [26] presented a multi-modal method to extract neuro-imaging features for AD diagnosis.”