Learning using privileged information improves neuroimaging-based CAD of Alzheimer’s disease: a comparative study

Li, Yan; Meng, Fanqing

doi:10.1007/s11517-019-01974-3

Cited by 20 publications

(6 citation statements)

References 32 publications

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“…In the CAD systems, machine learning is able to extract informative features that describe the inherent patterns from data and play a vital role in medical image analysis (Wernick et al, 2010;Wu et al, 2016;Erickson et al, 2017;Li et al, 2019). However, the structures of the medical images are very complex, and the feature selection step is still carried out by the human experts on the basis of their domainspecific knowledge.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant performance improvements over traditional machine learning in computer-aided diagnosis of brain disorders. This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis. We first provide a comprehensive overview of deep learning techniques and popular network architectures by introducing various types of deep neural networks and recent developments. We then review deep learning methods for computer-aided analysis of four typical brain disorders, including Alzheimer's disease, Parkinson's disease, Autism spectrum disorder, and Schizophrenia, where the first two diseases are neurodegenerative disorders and the last two are neurodevelopmental and psychiatric disorders, respectively. More importantly, we discuss the limitations of existing studies and present possible future directions.

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Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

et al. 2020

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“…• Supervised: AdaBoost with PI (Chen et al [21], Liu et al [22]), SVM+ for risk modelling (Ribeiro et al [23], [24]), SVM+ and multi-task learning (Liang and Cherkassky [25], Liang and Cherkassky [26], Liang et al [27], Cai and Cherkassky [28], Tang et al [29]), Regression Forests for facial feature detection with privileged head pose or gender (Yang and Patras [30]), image classification using privileged attributes, bounding box annotations, and textual descripting tags (Sharmanska et al [31], Sharmanska et al [32], Li et al [33], Wang and Ji [34], Yan et al [35], Rodríguez et al [36]), structured SVM (SSVM) prediction algorithm for image object localization using PI (Feyereisl et al [37]), unifying distillation and PI (Lopez-Paz et al [38]), multi-instance learning for action and event recognition with privileged web data (Niu et al [39]), knowledge transfer for neural networks (Vapnik and Izmailov [8]), image object detection using PI (Hoffman et al [40]), domain adaptation (Sarafianos et al [41]), multiview privileged SVMs (Tang et al [42]), deep learning under PI (Lambert et al [43]), PI for structured output prediction (Zhang et al [44]), label enhancement with multi-label learning (Zhu et al [45]), PI for the diagnosis of Alzheimer's disease (Li et al [46], Ganaie and Tanveer [47]), breast (Shaikh et al [48]) and liver (Zhang et al [49]) cancers, PI for image super-resolution using CNNs (Lee et al [50]), robust SVM+ (Li et al [9], Wu et al [51]), twin SVM with PI (Che et al [52]), robust twin SVM+ (Li et al [53]), Support Vector ...…”

Section: Related Work a Privileged Informationmentioning

confidence: 99%

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

Lange,

Smolyakov,

Burnaev

2023

IEEE Access

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One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data -Learning Using Privileged Information paradigm (LUPI, Vapnik and Vashist [1]). Sequential Minimal Optimization (SMO) method has been already developed for supervised Support Vector Machines (SVM) in Platt [2] and Keerthi et al. [3], for unsupervised (one-class) SVM in Schölkopf et al. [4], and for SVM with privileged information (SVM+) in Pechyony and Vapnik [5]. As can be seen, the missing brick in this research has long been a one-class SVM with privileged information (OC-SVM+). In this paper, we propose SMO algorithm for OC-SVM+ that significantly outperforms non-sequential algorithms for training the OC-SVM+ model. Its finite-time convergence is established. The experiments show how privileged information affects a descriptive domain in the space of original features. Comparative benchmark tests demonstrate that our algorithm is superior over interior point algorithms.

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“…Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017 [5]. Early unsupervised methods used autoencoders or restricted Boltzmann machine methods to extract features that were then used for the classification of Alzheimer's disease [6]- [8]. Supervised learning applied to the diagnosis of Alzheimer's disease has been particularly well studied compared to unsupervised methods.…”

Section: Introductionmentioning

confidence: 99%

AMIM: An Adaptive Weighted Multimodal Integration Model for Alzheimer’s Disease Classification

Ding¹,

Zeng²,

Wang³

et al. 2023

IJACSA

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Alzheimer's disease (AD) is an irreversible neurological disorder, so early medical diagnosis is extremely important. Magnetic resonance imaging (MRI) is one of the main medical imaging methods used clinically to detect and diagnose AD. However, most existing computer-aided diagnostic methods only use MRI slices for model architecture design. They ignore informational differences between all slices. In addition, physicians often use multimodal data, such as medical images and clinical information, to diagnose patients. The approach helps physicians to make more accurate judgments. Therefore, we propose an adaptive weighted multimodal integration model (AMIM) for AD classification. The model uses global information images, maximum information slices and clinical information as data inputs for the first time. It adopts adaptive weights integration method for classification. Experimental results show that our model achieves an accuracy of 99.00% for AD versus normal controls (NC), and 82.86% for mild cognitive impairment (MCI) versus NC. The proposed model achieves best classification performance in terms of accuracy, compared with most state-ofthe-art methods.

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Learning using privileged information improves neuroimaging-based CAD of Alzheimer’s disease: a comparative study

Cited by 20 publications

References 32 publications

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

AMIM: An Adaptive Weighted Multimodal Integration Model for Alzheimer’s Disease Classification

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