Proceedings ArticleDOI
Improving MRI-based diagnosis of Alzheimer's disease via an ensemble privileged information learning algorithm
Xiao Zheng,Jun Shi,Qi Zhang,Shihui Ying,Yan Li +4 more
- pp 456-459
TLDR
The experimental results demonstrate that the proposed RBM+ works well as an LUPI algorithm for feature learning, and the ensemble L UPI algorithm is superior to the traditional predictive models for the MRI-based AD diagnosis using the positron emission tomography as the privileged information.Abstract:
In clinical practice, the magnetic resonance imaging (MRI) is a prevalent neuroimaging technique for Alzheimer's disease (AD) diagnosis. As a learning using privileged information (LUPI) algorithm, SVM+ has shown its effectiveness on the classification of brain disorders, with single-modal neuroimaging samples for testing but multimodal neuroimaging samples for training. In this work, we propose to apply the multimodal restricted Boltzmann machines (RBM) as an LUPI algorithm for feature learning so as to form an RBM+ algorithm. Furthermore, an ensemble LUPI algorithm is developed, integrating SVM+ and RBM+ by the multiple kernel boosting based strategy. The experimental results demonstrate that the proposed RBM+ works well as an LUPI algorithm for feature learning, and the ensemble LUPI algorithm is superior to the traditional predictive models for the MRI-based AD diagnosis using the positron emission tomography as the privileged information.read more
Citations
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Journal ArticleDOI
Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease: A Review
Muhammad Tanveer,Bharat Richhariya,R. U. Khan,Aamir Rashid,Pritee Khanna,Mukesh Prasad,Chin-Teng Lin +6 more
TL;DR: A large number of novel and efficient automated techniques are needed for early diagnosis of Alzheimer’s disease, and many novel approaches to diagnosis are being developed.
Journal ArticleDOI
Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques
TL;DR: This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples, and sagittal-plane MRIs were, at least, as effective as MRI from other planes at identifying AD in early stages.
Journal ArticleDOI
Cascaded Multi-Column RVFL+ Classifier for Single-Modal Neuroimaging-Based Diagnosis of Parkinson's Disease
TL;DR: The experimental results on both the transcranial sonography data set and the magnetic resonance imaging data set for PD show that the proposed cmcRVFL+ algorithm achieves superior performance to all the compared algorithms, and suggest that it has the potential to be flexibly applied to various single-modal imaging based CAD.
Journal ArticleDOI
Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer's Disease Image Data Classification.
Robert Logan,Brian G. Williams,Maria Ferreira da Silva,Akash Indani,Nicolas Schcolnicov,Anjali Ganguly,Sean J. Miller +6 more
TL;DR: In this article, the authors explore potential interdisciplinary approaches for early detection and provide insight into recent advances on AD classification using 3D CNN architectures for multi-modal PET/MRI data.
References
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Journal ArticleDOI
Deep Learning in Medical Image Analysis
TL;DR: This review covers computer-assisted analysis of images in the field of medical imaging and introduces the fundamentals of deep learning methods and their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on.
Journal ArticleDOI
Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment
TL;DR: Three modalities of biomarkers are proposed to combine, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method, and shows considerably better performance, compared to the case of using an individual modality of biomarker.
Journal ArticleDOI
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.
TL;DR: A novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning that could hierarchically discover the complex latent patterns inherent in both MRI and PET.
Proceedings Article
A new learning paradigm: Learning using privileged information
Vladimir Vapnik,Akshay Vashist +1 more
TL;DR: Details of the new paradigm and corresponding algorithms are discussed, some new algorithms are introduced, several specific forms of privileged information are considered, and superiority of thenew learning paradigm over the classical learning paradigm when solving practical problems is demonstrated.
Journal ArticleDOI
Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation
TL;DR: This paper proposes a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples and shows that the SHFA and HFA outperform the existing HDA methods.
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