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 proceedingConference 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 languageEnglish
Title of host publicationMachine Learning in Medical Imaging - Second International Workshop, MLMI 2011, Held in Conjunction with MICCAI 2011, Proceedings
Pages241-248
Number of pages8
DOIs
Publication statusPublished - 2011 Oct 17
Externally publishedYes
Event2nd 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 Sep 182011 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7009 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd 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
CountryCanada
CityToronto, ON
Period11/9/1811/9/18

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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