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9 August 2018 False positive reduction of pulmonary nodules using three-channel samples
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Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108065G (2018) https://doi.org/10.1117/12.2502840
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China
Abstract
We propose a novel method for false positive reduction of pulmonary nodules using three-channel samples with different average thickness. A three-channel sample contains a patch centered on the candidate point as well as two patches at the k-th slice above and below the candidate point. Three-channel samples include rich spatial contextual information of pulmonary nodules, and can be trained with a low computational and storage requirement. The convolutional neural networks (CNNs) are constructed and optimized as the feature extractor and classifier of candidates in our study. A fusion method is proposed for fusing multiple prediction results of each candidate. Our method reports high sensitivities of 84.8% and 91.4% at 4 and 8 false positives per scan respectively on 888 CT scans released by the LUNA16 Challenge. The experimental results show that our method significantly reduces false positives in pulmonary nodule detection.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Guodong Zhang, Wanwan Zhang, Zhaoxuan Gong, Jing Bi, Yoohwan Kim, and Wei Guo "False positive reduction of pulmonary nodules using three-channel samples", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108065G (9 August 2018); https://doi.org/10.1117/12.2502840
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