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Proceedings Paper

Collaborative classifiers in CT colonography CAD
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Paper Abstract

Multiple classifiers working collaboratively can usually achieve better performance than any single classifier working independently. Our CT colonography computer-aided detection (CAD) system uses support vector machines (SVM) as the classifier. In this paper, we developed and evaluated two schemes to collaboratively apply multiple SVMs in the same CAD system. One is to put the classifiers in a sequence (SVM sequence) and apply them one after another; the other is to put the classifiers in a committee (SVM committee) and use the committee decision for the classification. We compared the sequence order (best-first, worst-first and random) in the SVM sequence and two decision functions in the SVM committee (majority vote and sum probability). The experiments were conducted on 786 CTC datasets, with 63 polyp detections. We used 10-fold cross validation to generate the FROC curves, and conducted 100 bootstraps to evaluate the performance variation. The result showed that collaborative classifiers performed much better than individual classifiers. The SVM sequence had slightly better accuracy than the SVM committee but also had bigger performance variation.

Paper Details

Date Published: 30 March 2007
PDF: 8 pages
Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65142H (30 March 2007); doi: 10.1117/12.708318
Show Author Affiliations
Jianhua Yao, National Institutes of Health (United States)
Jiang Li, National Institutes of Health (United States)
Ronald Summers, National Institutes of Health (United States)

Published in SPIE Proceedings Vol. 6514:
Medical Imaging 2007: Computer-Aided Diagnosis
Maryellen L. Giger; Nico Karssemeijer, Editor(s)

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