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.
@article{arxiv.1606.08288,
title = {Interpreting extracted rules from ensemble of trees: Application to computer-aided diagnosis of breast MRI},
author = {Cristina Gallego-Ortiz and Anne L. Martel},
journal= {arXiv preprint arXiv:1606.08288},
year = {2016}
}
Comments
presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY