A Comprehensive Methodology for Determining the Most Informative Mammographic Features
- 16 March 2013
- journal article
- research article
- Published by Springer Nature in Journal of Digital Imaging
- Vol. 26 (5) , 941-947
Abstract
No abstract availableKeywords
This publication has 30 references indexed in Scilit:
- The Mammographic Density of a Mass Is a Significant Predictor of Breast CancerRadiology, 2011
- Breast Tissue Composition and Susceptibility to Breast CancerJNCI: Journal of the National Cancer Institute, 2010
- Family History, Mammographic Density, and Risk of Breast CancerCancer Epidemiology, Biomarkers & Prevention, 2010
- The ACR BI-RADS® Experience: Learning From HistoryJournal of the American College of Radiology, 2009
- The WEKA data mining softwareACM SIGKDD Explorations Newsletter, 2009
- Validation of Results from Knowledge Discovery: Mass Density as a Predictor of Breast CancerJournal of Digital Imaging, 2009
- A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer DiagnosisAmerican Journal of Roentgenology, 2009
- Positive Predictive Value of Specific Mammographic Findings according to Reader and Patient VariablesRadiology, 2009
- Breast Density as a Predictor of Mammographic Detection: Comparison of Interval- and Screen-Detected CancersJNCI: Journal of the National Cancer Institute, 2000
- Using mutual information for selecting features in supervised neural net learningIEEE Transactions on Neural Networks, 1994