New statistical learning theory paradigms adapted to breast cancer diagnosis/classification using image and non-image clinical data.

Abstract

The automated decision paradigms presented in this work address the false positive (FP) biopsy occurrence in diagnostic mammography. An EP/ES stochastic hybrid and two kernelized Partial Least Squares (K-PLS) paradigms were investigated with following studies: methodology performance comparisonsautomated diagnostic accuracy assessments with two data sets. The findings showed: the new hybrid produced comparable results more rapidlythe new K-PLS paradigms train and operate Essentially in real time for the data sets studied. Both advancements are essential components for eventually achieving the FP reduction goal, while maintaining acceptable diagnostic sensitivities.