Title |
A Vector Machine Formulation with Application to the Computer-Aided Diagnosis of Breast Cancer from DCE-MRI Screening Examinations
|
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Published in |
Journal of Digital Imaging, July 2013
|
DOI | 10.1007/s10278-013-9621-8 |
Pubmed ID | |
Authors |
Jacob E. D. Levman, Ellen Warner, Petrina Causer, Anne L. Martel |
Abstract |
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. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 1 | 2% |
Canada | 1 | 2% |
Unknown | 47 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 24% |
Researcher | 7 | 14% |
Student > Bachelor | 4 | 8% |
Professor | 3 | 6% |
Professor > Associate Professor | 3 | 6% |
Other | 10 | 20% |
Unknown | 10 | 20% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 14 | 29% |
Computer Science | 7 | 14% |
Engineering | 6 | 12% |
Physics and Astronomy | 3 | 6% |
Agricultural and Biological Sciences | 2 | 4% |
Other | 5 | 10% |
Unknown | 12 | 24% |