Machine Learning for Clinical Diagnosis from Functional Magnetic Resonance Imaging

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 wellestablished classifiers as documented in extensive experimental results. This is the first time that machine learning techniques base...

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Lei Zhang 0002, Dimitris Samaras, Dardo Tomasi, Nora D. Volkow, Rita Goldstein
• Keywords: Active Human Brain, Brain Images, Computer Vision, CVPR 2005, Intensities Representing Brain, Observed 3d Brain, Significant Brain Activation