# Multi-Kernel Capsule Network for Schizophrenia Identification

arXiv: https://arxiv.org/abs/1907.12827 · HTML: https://mlsanity.com/paper/1907.12827/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 8 of 8)

- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Learning with Capsules: A Survey](https://mlsanity.com/paper/2206.02664/) (2022)
- [Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions](https://mlsanity.com/paper/2301.07496/) (2023)
- [Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis](https://mlsanity.com/paper/2308.12495/) (2023)
- [Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Diagnosis and Lateralization Analysis](https://mlsanity.com/paper/2304.01347/) (2023)
- [Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis](https://mlsanity.com/paper/2306.05297/) (2023)
- [Cross-BCI, A Cross-BCI-Paradigm Classifica-tion Model Towards Universal BCI Applications](https://mlsanity.com/paper/2508.09242/) (2025)
- [DLGE: Dual Local-Global Encoding for Generalizable Cross-BCI-Paradigm](https://mlsanity.com/paper/2509.07991/) (2025)

## Abstract (arXiv)

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.