Joint estimation of multiple clinical variables of neurological diseases from imaging patterns

Authors:
Yang Fan;Daniel Kaufer;Dinggang Shen
Affiliations:
National Laboratory of Pattern Recognition, CASIA, Beijing, China and Departments of Radiology, University of North Carolina, Chapel Hill;Neurology, University of North Carolina, Chapel Hill;Departments of Radiology, University of North Carolina, Chapel Hill
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 7
Cited 3

Bagging predictors

Machine Learning
Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research
An introduction to variable and feature selection

The Journal of Machine Learning Research
Pose estimation and tracking using multivariate regression

Pattern Recognition Letters
Predicting clinical variable from MRI features: application to MMSE in MCI

MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I

Ordinal ranking for detecting mild cognitive impairment and Alzheimer's disease based on multimodal neuroimages and CSF biomarkers

MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Multi-modal multi-task learning for joint prediction of clinical scores in Alzheimer's disease

MBIA'11 Proceedings of the First international conference on Multimodal brain image analysis
Predicting clinical scores using semi-supervised multimodal relevance vector regression

MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method to estimate multiple clinical variables associated with neurological pathologies from brain images, aiming to quantitatively evaluate continuous transition of neurological pathologies from the normal to diseased state. Built upon morphological measures derived from structural MR brain images, a Bayesian regression method is developed to jointly model multiple clinical variables for capturing their inherent correlations and suppressing noise. Coupled with a feature selection technique, the regression method is used to build a joint estimator of multiple clinical variables associated with Alzheimer's disease from structural MR brain images of elderly individuals. The cross-validation results demonstrate that the proposed method has superior performance over existing techniques.