False Positive Reduction of Pulmonary Nodules using Three-channel Samples
Proceedings of SPIE - The International Society for Optical Engineering
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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.
Convolution neural networks; False positive reduction; Pulmonary nodule; Three-channel sample
False Positive Reduction of Pulmonary Nodules using Three-channel Samples.
Proceedings of SPIE - The International Society for Optical Engineering, 10806