Multi-Kernel Capsule Network for Schizophrenia Identification

TL;DR

This paper introduces a multi-kernel capsule network tailored for schizophrenia detection, leveraging brain structure-aware kernels and vector dropout to improve accuracy and prevent overfitting, outperforming existing methods.

Contribution

The study proposes a novel multi-kernel capsule network that considers brain anatomy and introduces vector dropout, enhancing schizophrenia classification performance.

Findings

Outperforms state-of-the-art methods in schizophrenia identification

Effectively captures interregional brain connectivities at multiple scales

Provides insights into parameter settings and model characteristics

Abstract

Objective: Schizophrenia seriously affects the quality of life. To date, both simple (linear discriminant analysis) and complex (deep neural network) machine learning methods have been utilized to identify schizophrenia based on functional connectivity features. The existing simple methods need two separate steps (i.e., feature extraction and classification) to achieve the identification, which disables simultaneous tuning for the best feature extraction and classifier training. The complex methods integrate two steps and can be simultaneously tuned to achieve optimal performance, but these methods require a much larger amount of data for model training. Methods: To overcome the aforementioned drawbacks, we proposed a multi-kernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match with partition sizes of brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of widely-used dropout strategy in deep learning, we developed vector dropout in the capsule layer to prevent overfitting of the model. Results: The comparison results showed that the proposed method outperformed the state-of-the-art methods. Besides, we compared performances using different parameters and illustrated the routing process to reveal characteristics of the proposed method. Conclusion: MKCapsnet is promising for schizophrenia identification. Significance: Our study not only proposed a multi-kernel capsule network but also provided useful information in the parameter setting, which is informative for further studies using a capsule network for neurophysiological signal classification.