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Proceedings Paper

Ensemble 3D residual network (E3D-ResNet) for reduction of false-positive polyp detections in CT colonography
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Paper Abstract

We developed a novel ensemble three-dimensional residual network (E3D-ResNet) for the reduction of false positives (FPs) in computer-aided detection (CADe) of polyps on CT colonography (CTC). To capture the volumetric multiscale information of CTC images, each polyp candidate was represented with three different sizes of volumes of interest (VOIs), which were enlarged to a common size and were individually subjected to three 3D-ResNets. These 3D-ResNets were trained to calculate three polyp-likelihood probabilities, p1, p2 and p3, corresponding to each input VOI. The final polyp likelihood, p, was obtained as the maximum of p1, p2 and p3. We compared the classification performance of the E3D-ResNet with that of a non-ensemble 3D-ResNet, ensemble 2D-ResNet, and ensemble of 2D- and 3D-convolutional neural network (CNN) models. All models were trained and evaluated with 21,021 VOIs of polyps and 19,557 VOIs of FPs that were sampled with data augmentation from the CADe detections on the CTC data of 20 patients. We evaluated the classification performance of the models with receiver operating characteristics (ROC) analysis using cross-validation, where the area under the ROC curve (AUC) was used as the figure of merit. Preliminary results showed that AUC value (0.98) of the E3D-ResNet was significantly higher than that of the reference models (P < 0.001), indicating that the E3D-ResNet has the potential of substantially reducing the FPs in CADe of polyps on CTC.

Paper Details

Date Published: 18 March 2019
PDF: 7 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 1095013 (18 March 2019); doi: 10.1117/12.2512173
Show Author Affiliations
Tomoki Uemura, Kyushu Institute of Technology (Japan)
Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Janne J. Näppi, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Huimin Lu, Kyushu Institute of Technology (Japan)
Hyoungseop Kim, Kyushu Institute of Technology (Japan)
Rie Tachibana, Massachusetts General Hospital, Harvard Medical School (United States)
National Institute of Technology, Oshima College (Japan)
Toru Hironaka, Massachusetts General Hospital (United States)
Harvard Medical School (United States)
Hiroyuki Yoshida, Massachusetts General Hospital (United States)
Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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