Predicting clinical scores using semi-supervised multimodal relevance vector regression

Bo Cheng; Daoqiang Zhang; Songcan Chen; Dinggang Shen

doi:10.1007/978-3-642-24319-6_30

Predicting clinical scores using semi-supervised multimodal relevance vector regression

Bo Cheng, Daoqiang Zhang, Songcan Chen, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
Pages	241-248
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-24319-6_30
Publication status	Published - 2011
Externally published	Yes
Event	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 - Toronto, ON, Canada Duration: 2011 Sept 18 → 2011 Sept 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7009 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	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
Country/Territory	Canada
City	Toronto, ON
Period	11/9/18 → 11/9/18

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-642-24319-6_30

Cite this

Cheng, B., Zhang, D., Chen, S., & Shen, D. (2011). Predicting clinical scores using semi-supervised multimodal relevance vector regression. In Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings (pp. 241-248). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7009 LNCS). https://doi.org/10.1007/978-3-642-24319-6_30

Predicting clinical scores using semi-supervised multimodal relevance vector regression. / Cheng, Bo; Zhang, Daoqiang; Chen, Songcan et al.
Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings. 2011. p. 241-248 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7009 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cheng, B, Zhang, D, Chen, S & Shen, D 2011, Predicting clinical scores using semi-supervised multimodal relevance vector regression. in Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7009 LNCS, pp. 241-248, 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, Toronto, ON, Canada, 11/9/18. https://doi.org/10.1007/978-3-642-24319-6_30

Cheng B, Zhang D, Chen S, Shen D. Predicting clinical scores using semi-supervised multimodal relevance vector regression. In Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings. 2011. p. 241-248. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24319-6_30

Cheng, Bo ; Zhang, Daoqiang ; Chen, Songcan et al. / Predicting clinical scores using semi-supervised multimodal relevance vector regression. Machine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings. 2011. pp. 241-248 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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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.

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Predicting clinical scores using semi-supervised multimodal relevance vector regression

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