Nonlinear graph fusion for multi-modal classification of Alzheimer’s disease
Tong T, Gray K, Gao Q, Chen L, Rueckert D (2015)
Publication Type: Conference contribution
Publication year: 2015
Journal
Publisher: Springer Verlag
Book Volume: 9352
Pages Range: 77-84
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Munich, DEU
ISBN: 9783319248875
DOI: 10.1007/978-3-319-24888-2_10
Abstract
Recent studies have demonstrated that biomarkers from multiple modalities contain complementary information for the diagnosis of Alzheimer’s disease (AD) and its prodromal stage mild cognitive impairment (MCI). In order to fuse data from multiple modalities, most previous approaches calculate a mixed kernel or a similarity matrix by linearly combining kernels or similarities from multiple modalities. However, the complementary information from multi-modal data are not necessarily linearly related. In addition, this linear combination is also sensitive to the weights assigned to each modality. In this paper, we propose a nonlinear graph fusion method to efficiently exploit the complementarity in the multi-modal data for the classification of AD. Specifically, a graph is first constructed for each modality individually. Afterwards, a single unified graph is obtained via a nonlinear combination of the graphs in an iterative cross diffusion process. Using the unified graphs, we achieved classification accuracies of 91.8% between AD subjects and normal controls (NC), 79.5% between MCI subjects and NC and 60.2% in a three-way classification, which are competitive with state-of-the-art results.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Fuzhou University (FZU)
China (CN)
United Kingdom (GB)
Fuzhou University (FZU)
China (CN)
How to cite
APA:
Tong, T., Gray, K., Gao, Q., Chen, L., & Rueckert, D. (2015). Nonlinear graph fusion for multi-modal classification of Alzheimer’s disease. In Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 77-84). Munich, DEU: Springer Verlag.
MLA:
Tong, Tong, et al. "Nonlinear graph fusion for multi-modal classification of Alzheimer’s disease." Proceedings of the 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015, Munich, DEU Ed. Luping Zhou, Yinghuan Shi, Li Wang, Qian Wang, Springer Verlag, 2015. 77-84.
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