Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.

•We propose a novel framework, an ensemble of 2D CNNs using single views, for efficient and accurate false positive reduction in pulmonary nodule detection.•We introduce a fully automatic non-nodule categorization by utilizing an autoencoder and k-means clustering to extend the learning capability of our network.•The proposed framework utilizes 2D patches to improve memory usage and computational efficiency without a decrease in performance.