Levman Jacob E D, Warner Ellen, Causer Petrina, Martel Anne L
Institute of Biomedical Engineering, Department of Engineering Science, Old Road Campus Research Building, University of Oxford, Headington, Oxford, Oxfordshire, OX1 3PJ, UK,
J Digit Imaging. 2014 Feb;27(1):145-51. doi: 10.1007/s10278-013-9621-8.
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.
本研究探讨一种提出的向量机公式在乳腺癌计算机辅助诊断背景下应用于动态对比增强磁共振成像检查的情况。本文描述了一种生成特征测量值的方法，该方法可表征病变的血管异质性，还描述了一种监督学习公式，在本应用中，该公式是对传统支持向量机的一种改进。使用主成分分析从检查中提取空间变化的信号强度测量值，并使用称为支持向量机（SVM）的机器学习技术对结果进行分类。在随机自展验证试验中，发现一种替代向量机公式比既定的支持向量机产生的结果有所改进，得到的受试者操作特征曲线面积为0.82，这在本应用中相对于支持向量机技术代表了具有统计学意义的改进。
