Fast CapsNet for Lung Cancer Screening
This work addresses lung cancer screening by enhancing automated nodule detection, though it appears incremental as it builds on existing CapsNet methods for a specific medical domain.
The paper tackled the problem of lung nodule classification in CT scans for cancer screening by proposing a fast capsule network (CapsNet) with a dynamic routing mechanism and convolutional decoder, achieving a 3x speedup and improved accuracy over CNNs with limited training data.
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.