Multi-modality Canonical Feature Selection for Alzheimer’s Disease Diagnosis
Feature selection has been commonly regarded as an effective method to lessen the problem of high dimension and low sample size in medical image analysis. In this paper, we propose a novel multi-modality canonical feature selection method. Unlike the conventional sparse Multi-Task Learning (MTL) based feature selection method that mostly considered only the relationship between target response variables, we further consider the correlations between features of different modalities by projecting them into a canonical space determined by canonical correlation analysis. We call the projections as canonical representations. By setting the canonical representations as regressors in a sparse least square regression framework and by further penalizing the objective function with a new canonical regularizer on the weight coefficient matrix, we formulate a multi-modality canonical feature selection method. With the help of the canonical information of canonical representations and also a canonical regularizer, the proposed method selects canonical-cross-modality features that are useful for the tasks of clinical scores regression and multi-class disease identification. In our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we combine Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer’s disease diagnosis.
KeywordsPositron Emission Tomography Feature Selection Positron Emission Tomography Image Canonical Correlation Analysis Feature Selection Method
- 1.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. John Wiley & Sons (2012)Google Scholar
- 4.Liu, J., Ji, S., Ye, J.: Multi-task feature learning via efficient l2, 1-norm minimization. In: UAI, pp. 339–348 (2009)Google Scholar
- 8.Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A.J., Shen, L.: Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 115–123. Springer, Heidelberg (2011)CrossRefGoogle Scholar
- 13.Zhu, X., Suk, H.I., Shen, D.: Matrix-similarity based loss function and feature selection for Alzheimer’s disease diagnosis. In: CVPR (2014)Google Scholar