Multikernel Capsule Network for Schizophrenia Identification

doi:10.1109/TCYB.2020.3035282

Return

Multikernel Capsule Network for Schizophrenia Identification

2022-06-01

Tian Wang

Anastasios Bezerianos

Andrzej Cichocki

Junhua Li *

DOI：10.1109/TCYB.2020.3035282

Original

Save

View PDF

Abstract

En 中文

Schizophrenia seriously affects the quality of life. To date, both simple (e.g., linear discriminant analysis) and complex (e.g., 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. To overcome the aforementioned drawbacks, we proposed a multikernel capsule network (MKCapsnet), which was developed by considering the brain anatomical structure. Kernels were set to match partition sizes of the brain anatomical structure in order to capture interregional connectivities at the varying scales. With the inspiration of the widely used dropout strategy in deep learning, we developed capsule dropout in the capsule layer to prevent overfitting of the model. 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. MKCapsnet is promising for schizophrenia identification. Our study first utilized the capsule neural network for analyzing functional connectivity of magnetic resonance imaging (MRI) and proposed a novel multikernel capsule structure with the consideration of brain anatomical parcellation, which could be a new way to reveal brain mechanisms. In addition, we provided useful information in the parameter setting, which is informative for further studies using a capsule network for other neurophysiological signal classification.

Keywords:

Kernel

Biological neural networks

Routing

Functional magnetic resonance imaging

Feature extraction

Training

Diseases

Brain connectivity

deep learning (DL)

functional magnetic resonance imaging (fMRI)

multikernel capsule network (MKCapsnet)

schizophrenia diagnosis

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.