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Multisite Schizophrenia Classification Based on Brainnetome Atlas by Deep Learning | IEEE Conference Publication | IEEE Xplore

Multisite Schizophrenia Classification Based on Brainnetome Atlas by Deep Learning


Abstract:

The paper presents large-scale multisite schizophrenia classification using cutting-edge deep learning tools. In this study, we collected 1275 participants at 8 sites, in...Show More

Abstract:

The paper presents large-scale multisite schizophrenia classification using cutting-edge deep learning tools. In this study, we collected 1275 participants at 8 sites, including 662 schizophrenic patients. We combine the fine-grained Brainnetome Atlas to extract features. And we propose the generalized feature-invariant deep neural network framework to ensure the model generalization in automatic diagnosis schizophrenia. Our model evaluate with 10-fold cross-validation and leave-one-site validation prediction, Average accuracy brings 2.7\% gain in classification accuracy against standard leave-one-site-out validation. Besides, our model gives the promising result on all sites classification and great potentials for computer-aided diagnosis of psychiatric disorders with simple and meaningful biomarkers.
Date of Conference: 23-25 November 2018
Date Added to IEEE Xplore: 14 April 2019
ISBN Information:
Conference Location: Nanjing, China

References

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