CapNeXt: Unifying Capsule And Resnext For Medical Image Segmentation

Abstract

Capsule Network is a contemporary approach to image analysis that emphasizes part-whole relationships. However, its applications to segmentation tasks are limited due to training difficulties such as initialization and convergence. In this study, we propose a novel Capsule Network, called CapNeXt, that unifies Capsule and ResNeXt architectures for medical image segmentation. CapNeXt advances the existing capsule-based segmentation model by integrating optimization techniques from Convolutional Neural Networks (CNN) to make training much easier than other contemporary Capsule-based segmentation methods. Experimental results on two public datasets show that CapNeXt outperforms the CNNs and other Capsule architectures in 2D and 3D segmentation tasks by 1% of the Dice score. The code will be released on GitHub after being accepted.