Journal
JOURNAL OF DIGITAL IMAGING
Volume 27, Issue 1, Pages 145-151Publisher
SPRINGER
DOI: 10.1007/s10278-013-9621-8
Keywords
Breast; Cancer Detection; Computer-Aided Diagnosis; Machine Learning; Support Vector Machine; Receiver-Operating Characteristic Curve Analysis; Magnetic Resonance Imaging
Funding
- Canadian Breast Cancer Research Alliance
- Canadian Breast Cancer Foundation
- Canadian Institute for Health Research
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This study investigates the use of a proposed vector machine formulation with application to dynamic contrast-enhanced magnetic resonance imaging examinations in the context of the computer-aided diagnosis of breast cancer. This paper describes a method for generating feature measurements that characterize a lesion's vascular heterogeneity as well as a supervised learning formulation that represents an improvement over the conventional support vector machine in this application. Spatially varying signal-intensity measures were extracted from the examinations using principal components analysis and the machine learning technique known as the support vector machine (SVM) was used to classify the results. An alternative vector machine formulation was found to improve on the results produced by the established SVM in randomized bootstrap validation trials, yielding a receiver-operating characteristic curve area of 0.82 which represents a statistically significant improvement over the SVM technique in this application.
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