False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography: A Massive-Training Support Vector Regression Approach

机译：CT结肠造影术中息肉的计算机辅助检测中的假阳性减少：大规模训练支持向量回归方法

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A massive-training artificial neural network (MTANN) has been investigated for reduction of false positives (FPs) in computer-aided detection (CADe) of polyps in CT colonography (CTC). A major limitation of the MTANN is a long training time. To address this issue, we investigated the feasibility of a support vector regression (SVR) in the massive-training framework and developed a massive-training SVR (MTSVR). To test the proposed MTSVR, we compared it with the original MTANN in FP reduction in CADe of polyps in CTC. With MTSVR, we reduced the training time by a factor of 190, while achieving a performance (by-polyp sensitivity of 94.7% with 2.5 FPs/patient) comparable to that of the original MTANN (which has the same sensitivity with 2.6 FPs/patient).

机译：已经对大规模训练的人工神经网络（MTANN）进行了研究，以减少CT结肠造影（CTC）中息肉的计算机辅助检测（CADe）中的假阳性（FPs）。 MTANN的主要限制是训练时间长。为了解决此问题，我们研究了大规模训练框架中支持向量回归（SVR）的可行性，并开发了大规模训练SVR（MTSVR）。为了测试拟议的MTSVR，我们将其与原始MTANN进行了比较，以降低FP中CTC中息肉的CADe含量。借助MTSVR，我们将培训时间减少了190倍，同时获得了与原始MTANN相当的性能（与每名患者2.6 FP相同的敏感性）（息肉敏感性为94.7％，每名患者2.5 FP））。

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《Virtual colonoscopy and abdominal imaging : Computational challenges and clinical opportunities》|2010年|p.47-52|共6页
会议地点 Beijing(CN);Beijing(CN);Beijing(CN)
作者
Jian-Wu Xu; Kenji Suzuki;
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作者单位

Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637;

Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637;

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会议组织
原文格式 PDF
正文语种 eng
中图分类生物医学工程;在其他方面的应用;
关键词
colorectal cancer; computer-aided detection; false-positive reduction; pixel-based machine learning; support vector regression;

机译：大肠癌；计算机辅助检测；假阳性减少；基于像素的机器学习；支持向量回归;

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专利

1. Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography. [J] . Xu JW, Suzuki K Medical Physics . 2011,第4期

机译：大规模训练支持向量回归和高斯过程，用于在计算机辅助CT结肠造影术中检测息肉时降低假阳性。
2. Massive-Training Artificial Neural Network Coupled With Laplacian-Eigenfunction-Based Dimensionality Reduction for Computer-Aided Detection of Polyps in CT Colonography [J] . Suzuki K., Zhang J., Xu J. Medical Imaging, IEEE Transactions on . 2010,第11期

机译：大规模训练人工神经网络结合基于Laplacian特征函数的降维技术在CT结肠造影术中对息肉进行计算机辅助检测
3. Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. [J] . Suzuki K, Yoshida H, Nappi J, Medical Physics . 2008,第2期

机译：专家3D大规模训练ANN的混合物，可减少CAD中多种类型的误报，从而在CT结肠造影中检测息肉。
4. Principal-Component Massive-Training Machine- Learning Regression for False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography [C] . Kenji Suzuki, Jianwu Xu, Jun Zhang, Machine learning in medical imaging . 2010

机译：CT结肠摄影术中息肉的计算机辅助检测中虚假减少的主成分大规模训练机器学习回归
5. A new intrusion detection system based on the combination of support vectors and ant colony: Algorithm and implementation. [D] . Zhang, Qinglei. 2009

机译：一种基于支持向量和蚁群相结合的新型入侵检测系统：算法与实现。
6. Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography [O] . Jian-Wu Xu, Kenji Suzuki -1

机译：大规模训练支持向量回归和高斯过程用于CT结肠造影术中息肉的计算机辅助检测中的假阳性减少
7. Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography [O] . Xu, Jian-Wu, Suzuki, Kenji 2011

机译：大规模训练支持向量回归和高斯过程用于CT结肠造影术中息肉的计算机辅助检测中的假阳性减少

False-Positive Reduction in Computer-Aided Detection of Polyps in CT Colonography: A Massive-Training Support Vector Regression Approach

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