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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Singh, V., Mukherjee, L., & Chung, M. K. (2008). Cortical surface thickness as a classifier: Boosting for autism classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5241 LNCS, pp. 999–1007). https://doi.org/10.1007/978-3-540-85988-8_119
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