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首页> 外文会议>International Workshop on Machine Learning in Medical Imaging >Computer-Aided Detection of Polyps in CT Colonography with Pixel-Based Machine Learning Techniques
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Computer-Aided Detection of Polyps in CT Colonography with Pixel-Based Machine Learning Techniques

机译：基于像素的机器学习技术的CT主影中息肉的计算机辅助检测

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Pixel/voxel-based machine-learning techniques have been developed for classification between polyp regions of interest (ROIs) and non-polyp ROIs in computer-aided detection (CADe) of polyps in CT colonography (CTC). Although 2D/3D ROIs can be high-dimensional, they may reside in a lower dimensional manifold. We investigated the manifold structure of 2D CTC ROIs by use of the Laplacian eigenmaps technique. We compared a support vector machine (SVM) classifier with the Laplacian eigenmaps-based dimensionality-reduced ROIs with massive-training support vector regression (MTSVR) in reduction of false positive (FP) detections. The Laplacian eigenmaps-based SVM classifier removed 16.0% (78/489) of FPs without any loss of polyps in a leave-one-lesion-out cross-validation test, whereas the MTSVR removed 49.9% (244/489); thus, yielded a 96.6% by-polyp sensitivity at an FP rate of 2.4 (254/106) per patient.
机译：已经开发了像素/体素的机器学习技术，用于CT中息息肉（CTC）的息肉（CADE）的息肉（ROIS）和非息肉ROI之间的分类进行分类。虽然2D / 3D ROI可以高维，但它们可能驻留在较低的尺寸歧管中。我们通过使用Laplacian Eigenmaps技术来研究2D CTC ROI的歧管结构。我们将支持向量机（SVM）分类器与基于Laplacian Eigenmaps的维度降低的ROIS进行比较，具有大规模训练支持向量回归（MTSVR），减少了假阳性（FP）检测。基于Laplacian eIgenmaps的SVM分类器除去16.0％（78/489）的FPS，无需任何息肉丢失的息肉，而MTSVR除以49.9％（244/489）;因此，每位患者的FP速率产生96.6％的副息肉敏感性，每位患者为2.4（254/106）。

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