School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China.
Department of Electrical and Computer Engineering, McGill University, Montréal, QC, Canada.
Med Biol Eng Comput. 2018 Apr;56(4):571-582. doi: 10.1007/s11517-017-1712-0. Epub 2017 Aug 24.
Microwave-based breast cancer detection has been proposed as a complementary approach to compensate for some drawbacks of existing breast cancer detection techniques. Among the existing microwave breast cancer detection methods, machine learning-type algorithms have recently become more popular. These focus on detecting the existence of breast tumours rather than performing imaging to identify the exact tumour position. A key component of the machine learning approaches is feature extraction. One of the most widely used feature extraction method is principle component analysis (PCA). However, it can be sensitive to signal misalignment. This paper proposes feature extraction methods based on time-frequency representations of microwave data, including the wavelet transform and the empirical mode decomposition. Time-invariant statistics can be generated to provide features more robust to data misalignment. We validate results using clinical data sets combined with numerically simulated tumour responses. Experimental results show that features extracted from decomposition results of the wavelet transform and EMD improve the detection performance when combined with an ensemble selection-based classifier.
基于微波的乳腺癌检测方法已被提出作为一种补充方法，以弥补现有乳腺癌检测技术的一些缺陷。在现有的微波乳腺癌检测方法中，基于机器学习的算法最近变得越来越流行。这些方法侧重于检测乳房肿瘤的存在，而不是进行成像以确定确切的肿瘤位置。机器学习方法的一个关键组成部分是特征提取。最广泛使用的特征提取方法之一是主成分分析（PCA）。然而，它可能对信号不对齐敏感。本文提出了基于微波数据时频表示的特征提取方法，包括小波变换和经验模态分解。可以生成时不变统计量，以提供对数据不对齐更鲁棒的特征。我们使用结合数值模拟肿瘤响应的临床数据集验证了结果。实验结果表明，与基于集成选择的分类器结合使用时，从小波变换和 EMD 的分解结果中提取的特征可以提高检测性能。
