  Lung cancer is the leading cause of cancer-related deaths in the past several
years. A major challenge in lung cancer screening is the detection of lung
nodules from computed tomography (CT) scans. State-of-the-art approaches in
automated lung nodule classification use deep convolutional neural networks
(CNNs). However, these networks require a large number of training samples to
generalize well. This paper investigates the use of capsule networks (CapsNets)
as an alternative to CNNs. We show that CapsNets significantly outperforms CNNs
when the number of training samples is small. To increase the computational
efficiency, our paper proposes a consistent dynamic routing mechanism that
results in $3\times$ speedup of CapsNet. Finally, we show that the original
image reconstruction method of CapNets performs poorly on lung nodule data. We
propose an efficient alternative, called convolutional decoder, that yields
lower reconstruction error and higher classification accuracy.
