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首页> 外文期刊>Geometry, Imaging and Computing >Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography
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Adaptive kernel based multiple kernel learning for computer-aided polyp detection in CT colonography

机译：基于自适应核的多核学习技术在CT结肠造影中进行计算机辅助息肉检测

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摘要

Computer-aided detection (CAD) of colonic polyps, as a second reader for computed tomographic colonography (CTC) screening, has earned extensive research interest over the past decades. False positive (FP) reduction in the CAD system plays a crucial role in detecting the polyps. To improve the performance of FP reduction and better assist the physician's diagnosis, we propose an adaptive kernel based multiple kernel learning (MKL) method for CAD of colonic polyps, called AK-MKL. This method builds a more adaptive synthesized classifier by incorporating an adaptive kernel into a set of predefined base kernels for better performance in differentiating true polyps from FPs, which is implemented by learning an optimal combination of a collection of those kernel-based classifiers. Performance evaluation for the presented AK-MKL method was performed on a CTC database, consisting of 25 patients with 50 CT scans. In terms of the AUC (area under the curve of receiver operating characteristic) and accuracy merits, he experimental results showed that our AK-MKL method achieves better performance, compared with two other different methods, i.e., one classifier based on support vector machine (SVM) with only one adaptive kernel (AK-SVM) and the other one based on multiple kernel learning only (MKL).
机译：结肠息肉的计算机辅助检测（CAD）作为计算机断层扫描结肠造影（CTC）筛查的第二种阅读器，在过去几十年中赢得了广泛的研究兴趣。 CAD系统中假阳性（FP）的减少在检测息肉中起着至关重要的作用。为了提高FP减少的性能并更好地协助医生的诊断，我们提出了一种用于结肠息肉CAD的基于自适应核的多核学习（MKL）方法，称为AK-MKL。此方法通过将自适应内核合并到一组预定义的基本内核中，从而构建更好的自适应合成分类器，从而在区分真息肉和FP方面具有更好的性能，这是通过学习这些基于内核的分类器集合的最佳组合来实现的。对所提出的AK-MKL方法的性能评估是在CTC数据库上进行的，该数据库由25名患者进行了50次CT扫描。实验结果表明，就AUC（接收器工作特性曲线下的面积）和精度优劣而言，我们的AK-MKL方法与其他两种不同方法（即一种基于支持向量机的分类器（ SVM）仅具有一个自适应内核（AK-SVM），而另一个仅基于多内核学习（MKL）。

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