| 基于深度学习的fMRI数据分析在偏头痛研究中的应用 |
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| 引用本文： | 肖君超,曾卫明,杨嘉君,石玉虎,徐艳红,焦磊.基于深度学习的fMRI数据分析在偏头痛研究中的应用[J].计算机系统应用,2018,27(4):249-253. |
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| 作者姓名： | 肖君超 曾卫明 杨嘉君 石玉虎 徐艳红 焦磊 |
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| 作者单位： | 上海海事大学 信息工程学院, 上海 201306,上海海事大学 信息工程学院, 上海 201306,上海交通大学附属第六人民医院 神经内科, 上海 201306,上海海事大学 信息工程学院, 上海 201306,上海交通大学附属第六人民医院 神经内科, 上海 201306,上海交通大学附属第六人民医院 放射科, 上海 201306 |
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| 基金项目： | 国家自然科学基金（31470954）；上海市2014年度浦东新区科技发展基金创新资金（医疗卫生）项目（PKJ2014-Y08） |
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| 摘 要： | 偏头痛作为一种常见的疾病，发病概率高，致病机理尚不明确，并且临床缺乏有效的诊断手段.运用功能核磁共振成像技术获取被试脑功能数据，然后通过深度学习中自动编码器，自动提取数据特征，并结合各种机器学习算法，预测偏头痛，为临床诊断提供参考依据.用深度学习提取数据特征，训练分类器，能达到更好的分类效果.深度学习算法可以在传统模板获取初步特征之后，进一步提取更加精细有效的特征，在预测偏头痛上获得更好的分类性能.
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| 关 键 词： | 偏头痛 深度学习 功能磁共振 神经影像 自动编码器 |
| 收稿时间： | 2017/8/8 0:00:00 |
| 修稿时间： | 2017/8/28 0:00:00 |
fMRI Data Analysis Based on Deep Learning in the Application of Migraine |
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| XIAO Jun-Chao,ZENG Wei-Ming,YANG Jia-Jun,SHI Yu-Hu,XU Yan-Hong and JIAO Lei.fMRI Data Analysis Based on Deep Learning in the Application of Migraine[J].Computer Systems& Applications,2018,27(4):249-253. |
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| Authors: | XIAO Jun-Chao ZENG Wei-Ming YANG Jia-Jun SHI Yu-Hu XU Yan-Hong and JIAO Lei |
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| Affiliation: | College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China,College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China,Department of Neurology, the Sixth People''s Hospital Affiliated to Shanghai Jiaotong University, Shanghai 201306, China,College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China,Department of Neurology, the Sixth People''s Hospital Affiliated to Shanghai Jiaotong University, Shanghai 201306, China and Department of Radiology, the Sixth People''s Hospital Affiliated to Shanghai Jiaotong University, Shanghai 201306, China |
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| Abstract: | The migraine is a common disease with high incidence. It is still not easy to explain its pathogenesis very well. Therefore, it lacks effective diagnostic methods. This study aims to predict migraine by using the functional magnetic resonance imaging technology to obtain functional network of brain, then through deep learning of automatically it extracts data features by Autoencoder, combined with various machine learning algorithms to provide a reference for clinical diagnosis of physicians. It can get better classification effect to extract data features and train the classifier by deep learning. The deep learning algorithm, based on the initial features obtained by the traditional templates, can further extract more fine and effective features, and obtain better classification performance in predicting migraine. |
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| Keywords: | migraine deep learning fMRI neuroimaging autocoder |
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