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- W596392562 abstract "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 machinelearning algorithm arising from a well-founded theoretical framework, the Statistical Learning Theory, which has recentlyproved to be superior to the conventional Neural Network framework for both classification and regression tasks: indeed, theproposed 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 outputvalues ranging from 0 to 1 representing a measure of the presence in the subregion of a mass. A weighted sum of outputs overeach image is used to accomplish the FPR task. In the test phase, this approach reached promising results, retaining 87% ofmasses while reducing False Positives to 62%." @default.
- W596392562 created "2016-06-24" @default.
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- W596392562 date "2005-01-05" @default.
- W596392562 modified "2023-09-23" @default.
- W596392562 title "Support vector regression filtering for reduction of false positives in a mass detection cad scheme: preliminary results" @default.
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