Subclass-based multi-task learning for Alzheimer's disease diagnosis

Subclass-based multi-task learning for Alzheimer's disease diagnosis

In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underly...

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Main Authors: Suk, Heung-II, Lee, Seong-Whan, Shen, Dinggang
Format: Online
Language:English
Published: Frontiers Media S.A. 2014
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4124798/
id pubmed-4124798
recordtype oai_dc
spelling pubmed-41247982014-08-21 Subclass-based multi-task learning for Alzheimer's disease diagnosis Suk, Heung-II Lee, Seong-Whan Shen, Dinggang Neuroscience In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak1 distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and 74.04% (MCI-C vs. MCI-NC), respectively. Frontiers Media S.A. 2014-08-07 /pmc/articles/PMC4124798/ /pubmed/25147522 http://dx.doi.org/10.3389/fnagi.2014.00168 Text en Copyright © 2014 Suk, Lee, Shen and The Alzheimers Disease Neuroimaging Initiative. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type Open Access Journal
institution_category Foreign Institution
institution US National Center for Biotechnology Information
building NCBI PubMed
collection Online Access
language English
format Online
author Suk, Heung-II
Lee, Seong-Whan
Shen, Dinggang
spellingShingle Suk, Heung-II
Lee, Seong-Whan
Shen, Dinggang
Subclass-based multi-task learning for Alzheimer's disease diagnosis
author_facet Suk, Heung-II
Lee, Seong-Whan
Shen, Dinggang
author_sort Suk, Heung-II
title Subclass-based multi-task learning for Alzheimer's disease diagnosis
title_short Subclass-based multi-task learning for Alzheimer's disease diagnosis
title_full Subclass-based multi-task learning for Alzheimer's disease diagnosis
title_fullStr Subclass-based multi-task learning for Alzheimer's disease diagnosis
title_full_unstemmed Subclass-based multi-task learning for Alzheimer's disease diagnosis
title_sort subclass-based multi-task learning for alzheimer's disease diagnosis
description In this work, we propose a novel subclass-based multi-task learning method for feature selection in computer-aided Alzheimer's Disease (AD) or Mild Cognitive Impairment (MCI) diagnosis. Unlike the previous methods that often assumed a unimodal data distribution, we take into account the underlying multipeak1 distribution of classes. The rationale for our approach is that it is highly likely for neuroimaging data to have multiple peaks or modes in distribution, e.g., mixture of Gaussians, due to the inter-subject variability. In this regard, we use a clustering method to discover the multipeak distributional characteristics and define subclasses based on the clustering results, in which each cluster covers a peak in the underlying multipeak distribution. Specifically, after performing clustering for each class, we encode the respective subclasses, i.e., clusters, with their unique codes. In encoding, we impose the subclasses of the same original class close to each other and those of different original classes distinct from each other. By setting the codes as new label vectors of our training samples, we formulate a multi-task learning problem in a ℓ2,1-penalized regression framework, through which we finally select features for classification. In our experimental results on the ADNI dataset, we validated the effectiveness of the proposed method by improving the classification accuracies by 1% (AD vs. Normal Control: NC), 3.25% (MCI vs. NC), 5.34% (AD vs. MCI), and 7.4% (MCI Converter: MCI-C vs. MCI Non-Converter: MCI-NC) compared to the competing single-task learning method. It is remarkable for the performance improvement in MCI-C vs. MCI-NC classification, which is the most important for early diagnosis and treatment. It is also noteworthy that with the strategy of modality-adaptive weights by means of a multi-kernel support vector machine, we maximally achieved the classification accuracies of 96.18% (AD vs. NC), 81.45% (MCI vs. NC), 73.21% (AD vs. MCI), and 74.04% (MCI-C vs. MCI-NC), respectively.
publisher Frontiers Media S.A.
publishDate 2014
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4124798/
_version_ 1613121903523790848

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