MIXCAPS: A capsule network-based mixture of experts for lung nodule malignancy prediction

Abstract

Lung cancer is among the most common and deadliest cancers with a low 5-year survival rate. Timely diagnosis of lung cancer is, therefore, of paramount importance as it can save countless lives. In this regard, Computed Tomography (CT) scan is widely used for early detection of lung cancer, where human judgment is currently considered as the gold standard approach. Recently, there has been a surge of interest on development of automatic solutions via radiomics, as human-centered diagnosis is subject to inter-observer variability and is highly burdensome. Hand-crafted radiomics, serving as a radiologist assistant, requires fine annotations and pre-defined features. Deep learning radiomics solutions, however, have the promise of extracting the most useful features on their own in an end-to-end fashion without having access to the annotated boundaries. Among different deep learning models, Capsule Networks are proposed to overcome shortcomings of the Convolutional Neural Networks (CNNs) such as their inability to recognize detailed spatial relations. Capsule networks have so far shown satisfying performance in medical imaging problems. Capitalizing on their success, in this study, we propose a novel capsule network-based mixture of experts, referred to as the MIXCAPS. The proposed MIXCAPS architecture takes advantage of not only the capsule network’s capabilities to handle small datasets, but also automatically splitting dataset through a convolutional gating network. MIXCAPS enables capsule network experts to specialize on different subsets of the data. Our results show that MIXCAPS outperforms a single capsule network, a single CNN, a mixture of CNNs, and an ensemble of capsule networks, with an average accuracy of 90.7%, average sensitivity of 89.5%, average specificity of 93.4% and average area under the curve of 0.956. Our experiments also show that there is a relation between the gate outputs and a couple of hand-crafted features, illustrating explainable nature of the proposed MIXCAPS. To further evaluate generalization capabilities of the proposed MIXCAPS architecture, additional experiments on a brain tumor dataset are performed showing potentials of MIXCAPS for detection of tumors related to other organs.