Recent Advances in Reduction of False Positives in Computerized Detection of Polyps in CT Colonography

机译：CT结肠造影术中息肉的计算机化检测在减少假阳性方面的最新进展

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One of the major challenges in computer-aided detection (CADe) of polyps in CT colonography (CTC) is the reduction of false-positive detections (FPs) without a concomitant reduction in sensitivity. Major sources of FPs generated by CADe schemes include haustral folds, residual stool, rectal tubes, the ileocecal valve, and extra-colonic structures such as the small bowel and stomach. A large number of FPs is likely to confound the radiologist's task of image interpretation, lower the radiologist's efficiency, and cause radiologists to lose their confidence in CADe as a useful tool. Therefore, it is important to reduce the number of FPs as much as possible while maintaining a high sensitivity. In this paper, FP reduction techniques used in CADe schemes for detection of polyps in CTC are reviewed.

机译：CT结肠造影（CTC）中息肉的计算机辅助检测（CADe）的主要挑战之一是减少假阳性检测（FPs）而不降低灵敏度。 CADe方案产生的FPs的主要来源包括腹褶，残余粪便，直肠管，回盲瓣和结肠外结构，例如小肠和胃。大量FP可能会混淆放射线医师的图像解释任务，降低放射线医师的效率，并使放射线医师丧失对CADe作为有用工具的信心。因此，重要的是在保持高灵敏度的同时尽可能减少FP的数量。本文综述了CADe方案中用于检测CTC中息肉的FP减少技术。

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来源
《Virtual colonoscopy and abdominal imaging : Computational challenges and clinical opportunities》|2010年|p.32-39|共8页
会议地点 Beijing(CN);Beijing(CN);Beijing(CN)
作者
Kenji Suzuki;
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作者单位

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
classifier; pixel-based machine learning; computer-aided diagnosis; artificial neural network; support vector machine; colorectal cancer;

机译：分类器基于像素的机器学习；计算机辅助诊断；人工神经网络;支持向量机大肠癌;

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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. Improved False Positive Reduction by Novel Morphological Features for Computer-Aided Polyp Detection in CT Colonography [J] . Ren Yacheng, Ma Jingchen, Xiong Junfeng, Biomedical and Health Informatics, IEEE Journal of . 2019,第1期

机译：通过CT结肠造影中计算机辅助息肉检测的新形态学特征改进的假阳性率降低
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. Recent Advances in Reduction of False Positives inComputerized Detection of Polyps in CT Colonography [C] . Kenji Suzuki International Conference on Medical Image Computing and Computer-Assisted Intervention . 2011

机译：减少假阳性减少的预期输入CT中谱系中息肉的检测
5. Computerized detection of lung nodules with an enhanced false positive reduction scheme [D] . Memarian, Negar 2006

机译：增强的假阳性减少方案的肺结节的计算机化检测
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

Recent Advances in Reduction of False Positives in Computerized Detection of Polyps in CT Colonography

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