  High predictive performance and ease of use and interpretability are
important requirements for the applicability of a computer-aided diagnosis
(CAD) to human reading studies. We propose a CAD system specifically designed
to be more comprehensible to the radiologist reviewing screening breast MRI
studies. Multiparametric imaging features are combined to produce a CAD system
for differentiating cancerous and non-cancerous lesions. The complete system
uses a rule-extraction algorithm to present lesion classification results in an
easy to understand graph visualization.
