Nonlinear Metric Learning for Alzheimer’s Disease Diagnosis with Integration of Longitudinal Neuroimaging Features

Bibo Shi, Yani Chen, Kevin Hobbs, Charles D. Smith and Jundong Liu

Abstract

Identifying neuroimaging biomarkers of Alzheimer’s disease (AD) is of great importance for diagnosis and prognosis of the disease. In this study, we develop a novel nonlinear metric learning method to improve biomarker identification for Alzheimer’s disease and its early stage Mild Cognitive Impairment (MCI). Formulated under a constrained optimization framework, the proposed method learns a smooth nonlinear feature space transformation that pulls the samples of the same class closer to each other while pushing different classes further away. The thin-plate spline (TPS) is chosen as the geometric model due to its remarkable versatility and representation power in accounting for sophisticated deformations. In addition, a multi-resolution patch-based feature selection strategy is proposed to extract both cross-sectional and longitudinal features from MR brain images. Using the ADNI dataset, we evaluate the effectiveness of the proposed metric learning and feature extraction strategies and demonstrate the improvements over the state-of-the-art solutions within the same category.

Session

Poster 2

Files

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DOI

10.5244/C.29.138
https://dx.doi.org/10.5244/C.29.138

Citation

Bibo Shi, Yani Chen, Kevin Hobbs, Charles D. Smith and Jundong Liu. Nonlinear Metric Learning for Alzheimer’s Disease Diagnosis with Integration of Longitudinal Neuroimaging Features. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 138.1-138.13. BMVA Press, September 2015.

Bibtex

@inproceedings{BMVC2015_138,
	title={Nonlinear Metric Learning for Alzheimer’s Disease Diagnosis with Integration of Longitudinal Neuroimaging Features},
	author={Bibo Shi and Yani Chen and Kevin Hobbs and Charles D. Smith and Jundong Liu},
	year={2015},
	month={September},
	pages={138.1-138.13},
	articleno={138},
	numpages={13},
	booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
	publisher={BMVA Press},
	editor={Xianghua Xie, Mark W. Jones, and Gary K. L. Tam},
	doi={10.5244/C.29.138},
	isbn={1-901725-53-7},
	url={https://dx.doi.org/10.5244/C.29.138}
}