基于正则化多任务学习的精神分裂症 MRI 数据分类。

Using Regularized Multi-Task Learning for Schizophrenia MRI Data Classification.

机构信息

Beijing Key Laboratory of Big Data Technology for Food Safety, School of Artificial Intelligence, Beijing Technology and Business University, 100048 Beijing, China.

出版信息

J Integr Neurosci. 2022 Jun 24;21(4):119. doi: 10.31083/j.jin2104119.

DOI:10.31083/j.jin2104119

PMID:35864770

Abstract

BACKGROUND

Machine learning techniques and magnetic resonance imaging methods have been widely used in computer-aided diagnosis and prognosis of severe brain diseases such as schizophrenia, Alzheimer, etc. Methods: In this paper, a regularized multi-task learning method for schizophrenia classification is proposed, and three MRI datasets of schizophrenia, collected from different data centers, are investigated. Firstly, slice extraction is used in image preprocessing. Then texture features of gray-level co-occurrence matrices are extracted from the above processed images. Finally, a -norm regularized multi-task learning method is proposed to simultaneously learn the site-specific and site-shared features of the multi-site data, which can effectively discriminate schizophrenia patients from normal controls.

RESULTS

The classification error rate on 10 datasets can be reduced from 10% to 30%.

CONCLUSIONS

The proposed method obtains excellent results and provides objective evidence for clinical diagnosis and treatment of schizophrenia.

摘要

背景

机器学习技术和磁共振成像方法已广泛应用于精神分裂症、阿尔茨海默病等严重脑部疾病的计算机辅助诊断和预后。

方法

本文提出了一种针对精神分裂症分类的正则化多任务学习方法，并对来自不同数据中心的三个精神分裂症 MRI 数据集进行了研究。首先，在图像预处理中进行切片提取。然后，从上述处理后的图像中提取灰度共生矩阵的纹理特征。最后，提出了一种范数正则化多任务学习方法，以同时学习多站点数据的特定站点和共享站点特征，从而有效区分精神分裂症患者和正常对照者。