Joint coupled-feature representation and coupled boosting for AD diagnosis

Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
PublisherIEEE Computer Society
Pages2721-2728
Number of pages8
ISBN (Print)9781479951178, 9781479951178
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes
Event27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, United States
Duration: 2014 Jun 232014 Jun 28

Other

Other27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
CountryUnited States
CityColumbus
Period14/6/2314/6/28

Fingerprint

Neuroimaging
Brain
Data fusion
Large scale systems
Experiments

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Shi, Y., Suk, H-I., Gao, Y., & Shen, D. (2014). Joint coupled-feature representation and coupled boosting for AD diagnosis. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 2721-2728). [6909744] IEEE Computer Society. https://doi.org/10.1109/CVPR.2014.354

Joint coupled-feature representation and coupled boosting for AD diagnosis. / Shi, Yinghuan; Suk, Heung-Il; Gao, Yang; Shen, Dinggang.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. p. 2721-2728 6909744.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shi, Y, Suk, H-I, Gao, Y & Shen, D 2014, Joint coupled-feature representation and coupled boosting for AD diagnosis. in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition., 6909744, IEEE Computer Society, pp. 2721-2728, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, United States, 14/6/23. https://doi.org/10.1109/CVPR.2014.354
Shi Y, Suk H-I, Gao Y, Shen D. Joint coupled-feature representation and coupled boosting for AD diagnosis. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society. 2014. p. 2721-2728. 6909744 https://doi.org/10.1109/CVPR.2014.354
Shi, Yinghuan ; Suk, Heung-Il ; Gao, Yang ; Shen, Dinggang. / Joint coupled-feature representation and coupled boosting for AD diagnosis. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014. pp. 2721-2728
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