引入稳定学习的多中心脑磁共振影像统计分类方法研究  

Research on a Classification Approach for Multi-site Brain Magnetic Resonance Imaging Analysis by Introducing Stable Learning

作　　者：杨勃 钟志锴 YANG Bo;ZHONG Zhikai(School of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414006,China)

机构地区：[1]湖南理工学院信息科学与工程学院,湖南岳阳414006

出　　处：《湖南理工学院学报（自然科学版）》2024年第1期15-19,共5页Journal of Hunan Institute of Science and Technology(Natural Sciences)

基　　金：湖南省研究生科研创新项目(CX20221231,YCX2023A50);湖南省自然科学基金项目“面向小样本脑磁共振影像分析的数据生成技术与深度学习方法研究”(2024JJ7208)。

摘　　要：针对现有统计分析方法在多中心统计分类任务上缺乏稳定性的问题,提出一种引入稳定学习的多中心脑磁共振影像的统计分类方法.该方法使用多层3D卷积神经网络作为骨干结构,并引入稳定学习旁路结构调节卷积网络习得特征的稳定性.在稳定学习旁路中,首先使用随机傅里叶变换获取卷积网络特征的多路随机序列,然后通过学习和优化批次样本采样权重以获取卷积网络特征之间的独立性,从而改善跨中心分类泛化性.最后,在公开数据库FCP中的3中心脑影像数据集上进行跨中心性别分类实验.实验结果表明,与基准卷积网络相比,引入稳定学习的卷积网络具有更高的跨中心分类正确率,有效提高了跨中心泛化性和多中心统计分类的稳定性.Aiming at the lack of stability of existing statistical analysis methods suitable for single site tasks in a multi-site setting,a statistical classification approach integrating stable learning for multi-site brain magnetic resonance imaging(MRI)analysis tasks was proposed.In the proposed approach,a multi-layer 3-dimensional convolutional neural network(3D CNN)was used as the backbone structure,while a stable learning module used for improving the stability of features learning by CNN was integrated as bypassing structure.In the stable learning module,the random Fourier transform was firstly used to obtain the random sequences of CNN features,and then the independence between different sequences was obtained by optimizing sampling weights of every sample batch and improving the cross-site generalization.Finally,a cross-site gender classification experiment was conducted on the 3 brain MRI data site from the publicly available database FCP.The experimental results show that compared with the basic CNN,the CNN with stable learning has a higher accuracy in cross-site classification,and effectively improves the stability of cross-center generalization and multi-center statistical classification.

关 键 词：多中心脑磁共振影像分析 卷积神经网络 稳定学习 跨中心泛化 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]