Evaluation of an integrated multi-task machine learning system with humans in the loop
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Survey measures for evaluation of cognitive assistants
PerMIS '07 Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems
Classification of cell fates with support vector machine learning
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Exploiting temporal information in functional magnetic resonance imaging brain data
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Decoding visual brain states from fMRI using an ensemble of classifiers
Pattern Recognition
Biomedical image classification with random subwindows and decision trees
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Face Recognition System using Discrete Cosine Transform combined with MLP and RBF Neural Networks
International Journal of Mobile Computing and Multimedia Communications
Boosting with side information
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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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.