High-dimensional pattern regression using machine learning: From medical images to continuous clinical variables

Ying Wang, Yong Fan, Priyanka Bhatt, Christos Davatzikos

Research output: Contribution to journalArticle

Abstract

This paper presents a general methodology for high-dimensional pattern regression on medical images via machine learning techniques. Compared with pattern classification studies, pattern regression considers the problem of estimating continuous rather than categorical variables, and can be more challenging. It is also clinically important, since it can be used to estimate disease stage and predict clinical progression from images. In this work, adaptive regional feature extraction approach is used along with other common feature extraction methods, and feature selection technique is adopted to produce a small number of discriminative features for optimal regression performance. Then the Relevance Vector Machine (RVM) is used to build regression models based on selected features. To get stable regression models from limited training samples, a bagging framework is adopted to build ensemble basis regressors derived from multiple bootstrap training samples, and thus to alleviate the effects of outliers as well as facilitate the optimal model parameter selection. Finally, this regression scheme is tested on simulated data and real data via cross-validation. Experimental results demonstrate that this regression scheme achieves higher estimation accuracy and better generalizing ability than Support Vector Regression (SVR).

Original languageEnglish (US)
Pages (from-to)1519-1535
Number of pages17
JournalNeuroImage
Volume50
Issue number4
DOIs
StatePublished - May 1 2010

Keywords

  • Adaptive regional clustering
  • Alzheimer's disease
  • High-dimensionality pattern regression
  • MRI
  • Relevance vector regression

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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