Abstract
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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Xu, JW., Suzuki, K. (2011). Computer-Aided Detection of Polyps in CT Colonography with Pixel-Based Machine Learning Techniques. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2011. Lecture Notes in Computer Science, vol 7009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24319-6_44
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DOI: https://doi.org/10.1007/978-3-642-24319-6_44
Publisher Name: Springer, Berlin, Heidelberg
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