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.
Research on a Classification Approach for Multi-site Brain Magnetic Resonance Imaging Analysis by Introducing Stable Learning
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.
NETL
NSTL
万方数据
维普
