Linked Life Data

18979843

Source:http://linkedlifedata.com/resource/pubmed/id/18979843

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2008-11-4
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
11
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
999-1007
pubmed:dateRevised
2009-12-11
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Cortical surface thickness as a classifier: boosting for autism classification.
pubmed:affiliation
Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA. vsingh@biostat.wisc.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't