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Background and Objective: In pulmonary nodule detection, the first stage,candidate detection, aims to detect suspicious pulmonary nodules. However,detected candidates include many false positives and thus in the followingstage, false positive reduction, such false positives are reliably reduced.Note that this task is challenging due to 1) the imbalance between the numbersof nodules and non-nodules and 2) the intra-class diversity of non-nodules.Although techniques using 3D convolutional neural networks (CNNs) have shownpromising performance, they suffer from high computational complexity whichhinders constructing deep networks. To efficiently address these problems, wepropose a novel framework using the ensemble of 2D CNNs using single views,which outperforms existing 3D CNN-based methods.  Methods: Our ensemble of 2D CNNs utilizes single-view 2D patches to improveboth computational and memory efficiency compared to previous techniquesexploiting 3D CNNs. We first categorize non-nodules on the basis of featuresencoded by an autoencoder. Then, all 2D CNNs are trained by using the samenodule samples, but with different types of non-nodules. By extending thelearning capability, this training scheme resolves difficulties of extractingrepresentative features from non-nodules with large appearance variations. Notethat, instead of manual categorization requiring the heavy workload ofradiologists, we propose to automatically categorize non-nodules based on theautoencoder and k-means clustering.

Single-view 2D CNNs with Fully Automatic Non-nodule Categorization for False Positive Reduction in Pulmonary Nodule Detection