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Collaborative Classifiers in CT Colonography CAD

机译:CT结肠造影CAD中的协作分类器

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摘要

Multiple classifiers working collaboratively can usually achieve better performance than any single classifier working independently. Our CT colonography computer-aided detection (CAD) system uses support vector machines (SVM) as the classifier. In this paper, we developed and evaluated two schemes to collaboratively apply multiple SVMs in the same CAD system. One is to put the classifiers in a sequence (SVM sequence) and apply them one after another; the other is to put the classifiers in a committee (SVM committee) and use the committee decision for the classification. We compared the sequence order (best-first, worst-first and random) in the SVM sequence and two decision functions in the SVM committee (majority vote and sum probability). The experiments were conducted on 786 CTC datasets, with 63 polyp detections. We used 10-fold cross validation to generate the FROC curves, and conducted 100 bootstraps to evaluate the performance variation. The result showed that collaborative classifiers performed much better than individual classifiers. The SVM sequence had slightly better accuracy than the SVM committee but also had bigger performance variation.
机译:协同工作的多个分类器通常比单独工作的单个分类器可以实现更好的性能。我们的CT结肠造影计算机辅助检测(CAD)系统使用支持向量机(SVM)作为分类器。在本文中,我们开发和评估了两种方案,以在同一CAD系统中协同应用多个SVM。一种是将分类器置于一个序列(SVM序列)中,然后一个接一个地应用它们。另一种是将分类器放在一个委员会(SVM委员会)中,并使用委员会的决定进行分类。我们比较了SVM序列中的顺序顺序(最佳优先,最差优先和随机),并比较了SVM委员会中的两个决策功能(多数表决和和概率)。实验在786个CTC数据集上进行,检测到63次息肉。我们使用10倍交叉验证来生成FROC曲线,并进行了100次引导以评估性能差异。结果表明,协作分类器的性能要比单个分类器好得多。 SVM序列比SVM委员会具有更高的精度,但性能差异也更大。

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