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16 March 2020 Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography
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Abstract
Colorectal cancer is the second leading cause of cancer deaths worldwide. Computed tomographic colonography (CTC) can detect large colorectal polyps and cancers at a high sensitivity, whereas it can miss some of the smaller but still clinically significant 6 – 9 mm polyps. Dual-energy CTC (DE-CTC) can be used to provide more detailed information about scanned materials than does conventional single-energy CTC. We compared the classification performance of a 3D convolutional neural network (DenseNet) with those of four traditional 3D machine-learning models (AdaBoost, support vector machine, random forest, Bayesian neural network) and their cascade and ensemble classifier variants in the detection of small polyps in DE-CTC. Twenty patients with colonoscopy-confirmed polyps were examined by DE-CTC with a reduced one-day bowel preparation. The traditional machine-learning models were designed to identify polyps based on native radiomic dual-energy features of the DE-CTC image volumes. The performance of the machine-learning models was evaluated by use of the leave-one-patient-out method. The DenseNet was trained with a large independent external dataset of single-energy CTC cases and tested on blended image volumes of the DE-CTC cases. Although the DenseNet yielded the highest detection accuracy for typical polyps, AdaBoost and its cascade classifier variant yielded the highest overall polyp detection performance.
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Janne J. Näppi, Tomoki Uemura, Se Hyung Kim, Hyoungseop Kim, and Hiroyuki Yoshida "Comparative performance of 3D machine-learning and deep-learning models in the detection of small polyps in dual-energy CT colonography", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143C (16 March 2020); https://doi.org/10.1117/12.2549793
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