Support vector regression filtering for reduction of false positives in a mass detection cad scheme: preliminary results
2005
Reduction of False Positive signals (FPR) is a fundamental, yet awkward, step in computer aided mass detection schemes.
This paper describes preliminary results of a filtering approach to FPR based on Support Vector Regression (SVR), a machine
learning algorithm arising from a well-founded theoretical framework, the Statistical Learning Theory, which has recently
proved to be superior to the conventional Neural Network framework for both classification and regression tasks: indeed, the
proposed filtering method belongs to the family of neural filters.
The SVR filter is forced to associate subregions extracted from input images, masses and non-masses, to continuous output
values ranging from 0 to 1 representing a measure of the presence in the subregion of a mass. A weighted sum of outputs over
each image is used to accomplish the FPR task. In the test phase, this approach reached promising results, retaining 87% of
masses while reducing False Positives to 62%.
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