Support Vector Regression Filtering for Reduction of False Positives in a Mass Detection Cad Scheme: Preliminary Results

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%. “This work has been submitted to the IEEE ICIP05 for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.” SUPPORT VECTOR REGRESSION FILTERING FOR REDUCTION OF FALSE POSITIVES IN A MASS DETECTION CAD SCHEME: PRELIMINARY RESULTS Enrico Angelini, Renato Campanini and Alessandro Riccardi Department of Physics, University of Bologna, Italy Corresponding author: [email protected]