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 items, but also an indication of the "discriminative regions" on the cortical surface. We discuss the formulation and present interesting experimental results.
Singh
Vikas
V
Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA. vsingh@biostat.wisc.edu
Mukherjee
Lopamudra
L
Chung
Moo K
MK
D000465:Algorithms; D001185:Artificial Intelligence; D001321:Autistic Disorder / Q000473:pathology*; D002540:Cerebral Cortex / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity