Leveraging Coupled Interaction for Multimodal Alzheimer's Disease Diagnosis
- Authors
- Shi, Yinghuan; Suk, Heung-Il; Gao, Yang; Lee, Seong-Whan; Shen, Dinggang
- Issue Date
- 1월-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Feature extraction; Magnetic resonance imaging; Neuroimaging; Measurement; Kernel; Training; Brain modeling; Computer-aided AD; MCI diagnosis; coupled boosting (CB); coupled feature (CFR) representation; coupled metric ensemble (CME)
- Citation
- IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, v.31, no.1, pp.186 - 200
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
- Volume
- 31
- Number
- 1
- Start Page
- 186
- End Page
- 200
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/58524
- DOI
- 10.1109/TNNLS.2019.2900077
- ISSN
- 2162-237X
- Abstract
- As the population becomes older worldwide, accurate computer-aided diagnosis for Alzheimer's disease (AD) in the early stage has been regarded as a crucial step for neurodegeneration care in recent years. Since it extracts the low-level features from the neuroimaging data, previous methods regarded this computer-aided diagnosis as a classification problem that ignored latent featurewise relation. However, it is known that multiple brain regions in the human brain are anatomically and functionally interlinked according to the current neuroscience perspective. Thus, it is reasonable to assume that the extracted features from different brain regions are related to each other to some extent. Also, the complementary information between different neuroimaging modalities could benefit multimodal fusion. To this end, we consider leveraging the coupled interactions in the feature level and modality level for diagnosis in this paper. First, we propose capturing the feature-level coupled interaction using a coupled feature representation. Then, to model the modality-level coupled interaction, we present two novel methods: 1) the coupled boosting (CB) that models the correlation of pairwise coupled-diversity on both inconsistently and incorrectly classified samples between different modalities and 2) the coupled metric ensemble (CME) that learns an informative feature projection from different modalities by integrating the intrarelation and interrelation of training samples. We systematically evaluated our methods with the AD neuroimaging initiative data set. By comparison with the baseline learning-based methods and the state-of-the-art methods that are specially developed for AD/MCI (mild cognitive impairment) diagnosis, our methods achieved the best performance with accuracy of 95.0% and 80.7% (CB), 94.9% and 79.9% (CME) for AD/NC (normal control), and MCI/NC identification, respectively.
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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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