12 Advancements in Automated Diagnostic Mammography Using K-PLS Non-Linear Mappings Available to Purchase

This paper evaluated the contribution of both magnetic resonance mammography (MRM) image and non-image (NI) features (breast cancer risk factors) to benign ∕ malignant diagnostic accuracy using a new data set. This paper focus was using a new Kernealized-Partial Least Squares (K-PLS) and EP-SVM paradigms for this evaluation. An important initial finding is that (only) the non-imag , non-linear mapping region of the feature to diagnostic solution space contains significant diagnostic information, with a resultant overall A_z of 0.77. The MRI image features of mass size, mass shape and mass margin yielded an A_z of 0.96, which is excellent diagnostic performance. The EP-SVM A_z diagnostic results for the same basic data set and statistical cross validation methods were slightly lower because “outliers” were included in these analyses.