TY - GEN
T1 - Predicting clinical scores using semi-supervised multimodal relevance vector regression
AU - Cheng, Bo
AU - Zhang, Daoqiang
AU - Chen, Songcan
AU - Shen, Dinggang
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80053935178&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80053935178&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-24319-6_30
DO - 10.1007/978-3-642-24319-6_30
M3 - Conference contribution
AN - SCOPUS:80053935178
SN - 9783642243189
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 241
EP - 248
BT - Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
T2 - 2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
Y2 - 18 September 2011 through 18 September 2011
ER -