基于双通路卷积融合网络的脑龄分布预测

doi:10.6040/j.issn.1672-3961.0.2022.130

摘要/Abstract

摘要： 采用深度学习对脑龄预测问题进行研究,提出并设计一种基于双通路卷积融合网络的脑龄分布预测模型,以有效预测被试的大脑年龄。将被试静息态功能磁共振成像(rest-state functional MRI, rs-fMRI)数据通过标记分布学习方法,将确定的脑龄标签转化为一组具有高斯分布的概率,设计一个双通路卷积融合网络,包含卷积、批量归一化、池化等步骤,可以同时学习rs-fMRI多类激活图的特征,通过一个低秩融合网络来融合这些特征,利用损失函数对网络更新优化;对预测模型的结果进行详细分析。该模型得到的绝对平均误差和相关系数的指标分别为5.735和0.592 4。试验结果表明,相较于其他模型,该模型取得的平均绝对误差更小,相关系数更高,显著提高了基于rs-fMRI图像的脑龄预测精度。

关键词: 静息态功能磁共振成像, 激活图, 双通路卷积融合网络, 脑龄分布预测, 低秩融合

中图分类号:

U495

沈鑫杰,黄嘉爽,丁卫平,孙颖,王海鹏,鞠恒荣. 基于双通路卷积融合网络的脑龄分布预测[J]. 山东大学学报 (工学版), 2022, 52(6): 131-138.

SHEN Xinjie, HUANG Jiashuang, DING Weiping, SUN Ying, WANG Haipeng, JU Hengrong. Brain age distribution prediction with dual-pathway convolutional fusion neural networks[J]. Journal of Shandong University(Engineering Science), 2022, 52(6): 131-138.

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