Abstract
Effectively utilizing incomplete multi-modality data for diagnosis of Alzheimer’s disease (AD) is still an area of active research. Several multi-view learning methods have recently been developed to deal with missing data,with each view corresponding to a specific modality or a combination of several modalities. However,existing methods usually ignore the underlying coherence among views,which may lead to suboptimal learning performance. In this paper,we propose a view-aligned hypergraph learning (VAHL) method to explicitly model the coherence among the views. Specifically,we first divide the original data into several views based on possible combinations of modalities,followed by a sparse representation based hypergraph construction process in each view. A view-aligned hypergraph classification (VAHC) model is then proposed,by using a view-aligned regularizer to model the view coherence. We further assemble the class probability scores generated from VAHC via a multi-view label fusion method to make a final classification decision. We evaluate our method on the baseline ADNI-1 database having 807 subjects and three modalities (i.e.,MRI,PET,and CSF). Our method achieves at least a 4.6% improvement in classification accuracy compared with state-of-the-art methods for AD/MCI diagnosis.
Original language | English |
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Title of host publication | Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings |
Editors | Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal |
Publisher | Springer Verlag |
Pages | 308-316 |
Number of pages | 9 |
ISBN (Print) | 9783319467191 |
DOIs | |
Publication status | Published - 2016 |
Event | 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece Duration: 2016 Oct 21 → 2016 Oct 21 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9900 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 |
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Country/Territory | Greece |
City | Athens |
Period | 16/10/21 → 16/10/21 |
Bibliographical note
Funding Information:D. Shen—This study was supported in part by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, AG030514, and NS093842).
Publisher Copyright:
© Springer International Publishing AG 2016.
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science