Machine Learning for Clinical Diagnosis from Functional Magnetic Resonance Imaging Export

@inproceedings{Zhang05Machine, abstract = {Functional magnetic resonance imaging (fMRI) has enabled scientists to look into the active human brain. FMRI provides a sequence of 3D brain images with intensities representing brain activations. Standard techniques for fMRI analysis traditionally focused on finding the area of most significant brain activation for different sensations or activities. In this paper, we explore a new application of machine learning methods to a more challenging problem: classifying subjects into groups based on the observed 3D brain images when the subjects are performing the same task. Here we address the separation of drug-addicted subjects from healthy non-drug-using controls. In this paper, we explore a number of classification approaches. We introduce a novel algorithm that integrates side information into the use of boosting. Our algorithm clearly outperformed well-established classifiers as documented in extensive experimental results. This is the first time that machine learning techniques based on 3D brain images are applied to a clinical diagnosis that currently is only performed through patient self-report. Our tools can therefore provide information not addressed by traditional analysis methods and substantially improve diagnosis.}, author = {Zhang, Lei and Samaras, Dimitris and Volkow, Nora and Goldstein, Rita}, citeulike-article-id = {1496212}, citeulike-linkout-0 = {http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/cvpr/2005/2372/01/23721toc.xml&DOI=10.1109/CVPR.2005.219}, citeulike-linkout-1 = {http://www.cs.sunysb.edu/~samaras/papers/Zhang2005cvpr1.pdf}, journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)}, keywords = {adaboost, clinical-diagnosis, fisher-linear-discriminant, gaussian-naive-bayes, knn, mvpa, pca, subject-classification, svm, voxel-filtering}, pages = {1211--1217}, posted-at = {2007-07-26 15:59:27}, priority = {2}, title = {Machine Learning for Clinical Diagnosis from Functional Magnetic Resonance Imaging}, url = {http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/cvpr/2005/2372/01/23721toc.xml&DOI=10.1109/CVPR.2005.219}, volume = {1}, year = {2005} }

TY - CONF ID - Zhang05Machine L3 - citeulike-article-id:1496212 N2 - Functional magnetic resonance imaging (fMRI) has enabled scientists to look into the active human brain. FMRI provides a sequence of 3D brain images with intensities representing brain activations. Standard techniques for fMRI analysis traditionally focused on finding the area of most significant brain activation for different sensations or activities. In this paper, we explore a new application of machine learning methods to a more challenging problem: classifying subjects into groups based on the observed 3D brain images when the subjects are performing the same task. Here we address the separation of drug-addicted subjects from healthy non-drug-using controls. In this paper, we explore a number of classification approaches. We introduce a novel algorithm that integrates side information into the use of boosting. Our algorithm clearly outperformed well-established classifiers as documented in extensive experimental results. This is the first time that machine learning techniques based on 3D brain images are applied to a clinical diagnosis that currently is only performed through patient self-report. Our tools can therefore provide information not addressed by traditional analysis methods and substantially improve diagnosis. JF - IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) EP - 1217 TI - Machine Learning for Clinical Diagnosis from Functional Magnetic Resonance Imaging VL - 1 SP - 1211 KW - adaboost KW - clinical-diagnosis KW - fisher-linear-discriminant KW - gaussian-naive-bayes KW - knn KW - mvpa KW - pca KW - subject-classification KW - svm KW - voxel-filtering AU - Zhang, Lei AU - Samaras, Dimitris AU - Volkow, Nora AU - Goldstein, Rita PY - 2005/// UR - http://csdl2.computer.org/persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/cvpr/2005/2372/01/23721toc.xml&DOI=10.1109/CVPR.2005.219 ER -