Microwave breast cancer detection via cost-sensitive ensemble classifiers: Phantom and patient investigation
Microwave breast cancer detection via cost-sensitive ensemble classifiers: Phantom and patient investigation
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DOI：
10.1016/j.bspc.2016.09.003
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发表时间：
2017-01-01
影响因子：
5.1
通讯作者：
Coates, Mark
中科院分区：
文献类型：
--
作者：
Li, Yunpeng;Porter, Emily;Coates, Mark
微波乳房筛查已被提议作为目前X射线乳房X光检查标准的补充方式。在这项工作中，我们设计了三个集成分类结构，融合来自多个传感器的信息，以检测乳房的异常。一个原则性的Neyman-Pearson方法被开发，以允许控制假阳性率和假阴性率之间的权衡。我们评估性能使用的数据来自测量异质乳房假体。我们还使用了一项临床试验中收集的数据，该试验在8个月的时间内每月监测12名健康患者。为了评估所提出的算法的有效性，我们通过人为地将模拟的肿瘤反应添加到现有的健康志愿者的扫描中来模拟具有恶性病变的乳房的扫描。基于乳腺组织的测量特性和真实的乳腺测量结果构建肿瘤响应，因此仿真模型考虑了乳腺组织的异质性。我们提出的算法利用来自其他患者的乳房扫描或组织模仿乳房体模来了解乳房内容以及构成“无肿瘤”和“有肿瘤”测量集的内容。我们证明，集成选择为基础的算法，它构建了一个合奏的最翔实的分类器，显着优于其他检测技术的临床试验数据集。(C)2016爱思唯尔有限公司版权所有
Microwave breast screening has been proposed as a complementary modality to the current standard of X-ray mammography. In this work, we design three ensemble classification structures that fuse information from multiple sensors to detect abnormalities in the breast. A principled Neyman-Pearson approach is developed to allow control of the trade-off between false positive rate and the false negative rate. We evaluate performance using data derived from measurements of heterogeneous breast phantoms. We also use data collected in a clinical trial that monitored 12 healthy patients monthly over an eight month period. In order to assess the efficacy of the proposed algorithms we model scans of breasts with malignant lesions by artificially adding simulated tumour responses to existing scans of healthy volunteers. Tumour responses are constructed based on measured properties of breast tissues and real breast measurements, thus the simulation model takes into account the heterogeneity of the breast tissue. The algorithms we present take advantage of breast scans from other patients or tissue-mimicking breast phantoms to learn about breast content and what constitutes a "tumour-free" and "tumour-bearing" set of measurements. We demonstrate that the ensemble selection-based algorithm, which constructs an ensemble of the most informative classifiers, significantly outperforms other detection techniques for the clinical trial data set. (C) 2016 Elsevier Ltd. All rights reserved.