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MICCAI
2008
Springer

Cortical Surface Thickness as a Classifier: Boosting for Autism Classification

15 years 13 hour ago
Cortical Surface Thickness as a Classifier: Boosting for Autism Classification
We study the problem of classifying an autistic group from controls using structural image data alone, a task that requires a clinical interview with a psychologist. Because of the highly convoluted brain surface topology, feature extraction poses the first obstacle. A clinically relevant measure called the cortical thickness has shown promise but yields a rather challenging learning problem ? where the dimensionality of the distribution is extremely large and the training set is small. By observing that each point on the brain cortical surface may be treated as a "hypothesis", we propose a new algorithm for LPBoosting (with truncated neighborhoods) for this problem. In addition to learning a high quality classifier, our model incorporates topological priors into the classification framework directly ? that two neighboring points on the cortical surface (hypothesis pairs) must have similar discriminative qualities. As a result, we obtain not just a label {+1, -1} for test ite...
Vikas Singh, Lopamudra Mukherjee, Moo K. Chung
Added 06 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where MICCAI
Authors Vikas Singh, Lopamudra Mukherjee, Moo K. Chung
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