23 February 2012 Computer-aided detection of polyps in CT colonography by means of AdaBoost
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Abstract
Computer-aided detection (CADe) has been investigated for assisting radiologists in detecting polyps in CT colonography (CTC). One of the major challenges in current CADe of polyps in CTC is to improve the specificity without sacrificing the sensitivity. We have developed several CADe schemes based on a massive-training framework with different nonlinear regression models such as neural network regression, support vector regression, and Gaussian process regression. Individual CADe schemes based on different nonlinear regression models, however, achieved comparable results. In this paper, we propose to use the AdaBoost algorithm to combine different regression models in CADe schemes for improving the specificity without sacrificing the sensitivity. To test the performance of the proposed approach, we compared it with individual regression models in the distinction between polyps and various types of false positives (FPs). Our CTC database consisted of 246 CTC datasets obtained from 123 patients in the supine and prone positions. The testing set contained 93 patients including 19 polyps in seven patients and 86 negative patients with 474 FPs produced by an original CADe scheme. The AdaBoost algorithm combining multiple massive-training regression models achieved a performance that was higher than each individual regression model, yielding a 94.7% (18/19) bypolyp sensitivity at an FP rate of 2.0 (188/93) per patient in a leave-one-lesion-out cross validation test.
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Jian-Wu Xu, Jian-Wu Xu, Kenji Suzuki, Kenji Suzuki, } "Computer-aided detection of polyps in CT colonography by means of AdaBoost", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150V (23 February 2012); doi: 10.1117/12.912048; https://doi.org/10.1117/12.912048
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