Multi-view ensemble learning for dementia diagnosis from neuroimaging: an artificial neural network approach

Liu, Jiajun, Shang, Shuo, Zheng, Kai and Wen, Ji-Rong (2016) Multi-view ensemble learning for dementia diagnosis from neuroimaging: an artificial neural network approach. Neurocomputing, 195 8: 112-116. doi:10.1016/j.neucom.2015.09.119


Author Liu, Jiajun
Shang, Shuo
Zheng, Kai
Wen, Ji-Rong
Title Multi-view ensemble learning for dementia diagnosis from neuroimaging: an artificial neural network approach
Journal name Neurocomputing   Check publisher's open access policy
ISSN 1872-8286
0925-2312
Publication date 2016-06-26
Year available 2016
Sub-type Article (original research)
DOI 10.1016/j.neucom.2015.09.119
Open Access Status Not Open Access
Volume 195
Issue 8
Start page 112
End page 116
Total pages 5
Place of publication Amsterdam, Netherlands
Publisher Elsevier BV
Language eng
Subject 1706 Computer Science Applications
2805 Cognitive Neuroscience
1702 Artificial Intelligence
Abstract Identifying abnormalities from neuroimaging of brain matters has been a crucial way of diagnosis of two closely associated diseases, namely Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). Different types of neuroimaging have been developed to help such diagnosis, and significant research efforts are put into the automation and quantification of such diagnosis by computer algorithms over the past decades. In this paper we propose an ensemble learning framework to create effective models for AD/MCI related classification tasks from multiple modalities of neuroimaging and multiple baseline estimators. The framework is based on artificial neural networks and it resembles a composite model that solves the feature fusion learning problem as well as the prediction problem simultaneously, which targets at exploiting the prediction power of both fusing multiple data modalities and leveraging multiple mutually complementary classification models. We conduct extensive experiments on the well-known ADNI dataset and find that the proposed model works demonstrate advantages for both of the classification tasks studied.
Keyword Artificial neural networks
Ensemble learning
Neuroimaging
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
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