Diagnosis of Alzheimer's Disease Using View-Aligned Hypergraph Learning with Incomplete Multi-modality Data [chapter]

Mingxia Liu, Jun Zhang, Pew-Thian Yap, Dinggang Shen
2016 Lecture Notes in Computer Science  
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 viewaligned hypergraph
more » ... rning (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 viewaligned 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. Various approaches have been developed to deal with the problem of incomplete multimodality data. A straightforward method is to remove subjects with missing data. This approach, however, significantly reduces the sample size. An alternative way is to impute the
doi:10.1007/978-3-319-46720-7_36 pmid:28066842 pmcid:PMC5207479 fatcat:nlhwmg7d2jhsplmexhwhvoyidm