Predicting clinical scores using semi-supervised multimodal relevance vector regression

Authors:
Bo Cheng;Daoqiang Zhang;Songcan Chen;Dinggang Shen
Affiliations:
Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China and Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC;Dept. of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China;Dept. of Radiology and BRIC, University of North Carolina at Chapel Hill, NC
Venue:
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Year:
2011

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Abstract

We present a novel semi-supervised multimodal relevance vector regression (SM-RVR) method for predicting clinical scores of neurological diseases from multimodal brain images, to help evaluate pathological stage and predict future progression of diseases, e.g., Alzheimer's diseases (AD). Different from most existing methods, we predict clinical scores from multimodal (imaging and biological) biomarkers, including MRI, FDG-PET, and CSF. Also, since mild cognitive impairment (MCI) subjects generally contain more noises in their clinical scores compared to AD and healthy control (HC) subjects, we use only their multimodal data (i.e., MRI, FDG-PET and CSF), not their clinical scores, to train a semi-supervised model for enhancing the estimation of clinical scores for AD and healthy control (HC). Experimental results on ADNI dataset validate the efficacy of the proposed method.