[1]段逸凡,王瑜,付常洋,等.基于双模态磁共振成像和决策层融合的抑郁症辅助诊断[J].中国医学物理学杂志,2022,39(3):378-383.[doi:DOI:10.3969/j.issn.1005-202X.2022.03.019]
 DUAN Yifan,WANG Yu,FU Changyang,et al.Auxiliary diagnosis of depression based on bimodal magnetic resonance imaging and decision level fusion[J].Chinese Journal of Medical Physics,2022,39(3):378-383.[doi:DOI:10.3969/j.issn.1005-202X.2022.03.019]
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基于双模态磁共振成像和决策层融合的抑郁症辅助诊断()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
39卷
期数:
2022年第3期
页码:
378-383
栏目:
医学人工智能
出版日期:
2022-03-28

文章信息/Info

Title:
Auxiliary diagnosis of depression based on bimodal magnetic resonance imaging and decision level fusion
文章编号:
1005-202X(2022)03-0378-06
作者:
段逸凡王瑜付常洋肖洪兵邢素霞
北京工商大学人工智能学院, 北京 100048
Author(s):
DUAN Yifan WANG Yu FU Changyang XIAO Hongbing XING Suxia
School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
关键词:
抑郁症结构磁共振成像功能磁共振成像数据融合
Keywords:
Keywords: depression structural magnetic resonance imaging functional magnetic resonance imaging data fusion
分类号:
R318;R749.41
DOI:
DOI:10.3969/j.issn.1005-202X.2022.03.019
文献标志码:
A
摘要:
本研究提出一种基于结构和功能双模态磁共振成像数据融合的抑郁症分类算法,首先利用功能脑网络和深度学习网络分别提取功能和结构磁共振成像数据特征,并计算类概率,然后使用软投票法和加权投票法在决策层对两种类概率数据进行融合,充分提取功能与结构磁共振成像的数据信息,得到更加准确的分类效果。试验结果表明,数据融合方法可以显著提高抑郁症分类效果,获得91.34%的准确率和96.62%的召回率,更好地实现了抑郁症的辅助诊断与预后。
Abstract:
Abstract: A depression classification algorithm based on structural and functional magnetic resonance imaging data fusion is proposed. After extracting functional and structural data features by functional brain network and deep learning network, and obtaining class probability, soft voting method and weighted voting method are used to fuse two kinds of probability data at decision level for fully extracting the data information of functional and structural magnetic resonance imaging, thereby obtaining more accurate classification results. The test results show that the data fusion method can effectively improve the performance in depression classification, achieving an accuracy of 91.34% and a recall rate of 96.62%, which testifies that the proposed method can better realize the auxiliary diagnosis and prognosis of depression.

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备注/Memo

备注/Memo:
【收稿日期】2021-09-06 【基金项目】北京市自然科学基金-北京市教育委员会科技计划重点项目(KZ202110011015);国家自然科学基金(61671028) 【作者简介】段逸凡,硕士,主要从事图像处理、机器学习方面的研究,E-mail: 1106811434@qq.com 【通信作者】王瑜,博士后,教授,博士生导师,CCF会员,研究方向:图像处理、模式识别,E-mail: wangyu@btbu.edu.cn
更新日期/Last Update: 2022-03-28