SVM Based Feature Screening Applied to Hierarchical Cervial Cancer Detection

Jiayong Zhang, Yanxi Liu, and Tong Zhao
International Conference on Diagnostic Imaging and Analysis (ICDIA 2002), August, 2002.


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Abstract
We present a novel feature screening method by deriving relevance measures from the decision boundary of Support Vector Machine, which has several advantages over traditional screening methods based on Information Gain and Augmented Variance Ratio. The new algorithm is applied to a bottom-up approach to cervical cancer detection in multispectral PAP smear images that has been recently proposed by the authors. Comparative experiments show significant improvements on pixel-level classification accuracy using the new feature screening method.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Lab(s) / Group(s): Medical Robotics and Computer Assisted Surgery and Biomedical Image Analysis
Associated Project(s): Non-Invasive Optical Imaging in vivo for Early Detection and Advanced Diagnosis of Cancer

Text Reference
Jiayong Zhang, Yanxi Liu, and Tong Zhao, "SVM Based Feature Screening Applied to Hierarchical Cervial Cancer Detection," International Conference on Diagnostic Imaging and Analysis (ICDIA 2002), August, 2002.

BibTeX Reference
@inproceedings{Zhang_2002_4033,
   author = "Jiayong Zhang and Yanxi Liu and Tong Zhao",
   title = "SVM Based Feature Screening Applied to Hierarchical Cervial Cancer Detection",
   booktitle = "International Conference on Diagnostic Imaging and Analysis (ICDIA 2002)",
   month = "August",
   year = "2002",
}