Abstract
Recently, there has been a great interest in computer- aided Alzheimer's Disease (AD) and Mild Cognitive Im- pairment (MCI) diagnosis. Previous learning based meth- ods defined the diagnosis process as a classification task and directly used the low-level features extracted from neu- roimaging data without considering relations among them. However, from a neuroscience point of view, it's well known that a human brain is a complex system that multiple brain regions are anatomically connected and functionally inter- act with each other. Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging da- ta are related to each other in some ways. To this end, in this paper, we first devise a coupled feature representa- tion by utilizing intra-coupled and inter-coupled interaction relationship. Regarding multi-modal data fusion, we pro- pose a novel coupled boosting algorithm that analyzes the pairwise coupled-diversity correlation between modalities. Specifically, we formulate a new weight updating function, which considers both incorrectly and inconsistently classi- fied samples. In our experiments on the ADNI dataset, the proposed method presented the best performance with accu- racies of 94.7% and 80.1% for AD vs. Normal Control (NC) and MCI vs. NC classifications, respectively, outperforming the competing methods and the state-of-the-art methods.
Original language | English |
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Title of host publication | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
Publisher | IEEE Computer Society |
Pages | 2721-2728 |
Number of pages | 8 |
ISBN (Electronic) | 9781479951178, 9781479951178 |
DOIs | |
Publication status | Published - 2014 Sept 24 |
Event | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States Duration: 2014 Jun 23 → 2014 Jun 28 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
Other
Other | 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 |
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Country/Territory | United States |
City | Columbus |
Period | 14/6/23 → 14/6/28 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition