论文标题
fMRI多个丢失值由经常性Denoiser插入正规
fMRI Multiple Missing Values Imputation Regularized by a Recurrent Denoiser
论文作者
论文摘要
功能磁共振成像（fMRI）是一种神经影像学技术，由于其科学和临床应用，其重要性是重要的。与任何广泛使用的成像方式一样，有必要确保相同的质量，由于存在伪影或亚最佳成像分辨率，因此缺失值高度频繁。我们的工作集中于在多元信号数据上插入缺失值。为此，提出了一种新的插补方法，该方法是由两个主要步骤组成的：依赖空间的信号插补和对估算信号的时间依赖性正则化。在这项工作中提出了一个新颖的层，用于深度学习体系结构，从而带回了链式方程式的概念。最后，应用了一个经常性层来调整信号，从而捕获其真实模式。这两项操作都针对最先进的替代方案提高了鲁棒性。
Functional Magnetic Resonance Imaging (fMRI) is a neuroimaging technique with pivotal importance due to its scientific and clinical applications. As with any widely used imaging modality, there is a need to ensure the quality of the same, with missing values being highly frequent due to the presence of artifacts or sub-optimal imaging resolutions. Our work focus on missing values imputation on multivariate signal data. To do so, a new imputation method is proposed consisting on two major steps: spatial-dependent signal imputation and time-dependent regularization of the imputed signal. A novel layer, to be used in deep learning architectures, is proposed in this work, bringing back the concept of chained equations for multiple imputation. Finally, a recurrent layer is applied to tune the signal, such that it captures its true patterns. Both operations yield an improved robustness against state-of-the-art alternatives.
