IEEE/ACM Trans Comput Biol Bioinform. 2024 Jul-Aug;21(4):582-595. doi: 10.1109/TCBB.2023.3252577. Epub 2024 Aug 8.
Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it is a natural way to organise these data as tensors prior to performing automated analyses such as discrimination of neurological disorders like Parkinson's Disease (PD) and Attention Deficit and Hyperactivity Disorder (ADHD). However, the existing approaches are often subject to performance bottlenecks (e.g., conventional feature extraction and deep learning based feature construction), as these can lose the structural information that correlates multiple data dimensions or/and demands excessive empirical and application-specific settings. This study proposes a Deep Factor Learning model on a Hilbert Basis tensor (namely, HB-DFL) to automatically derive latent low-dimensional and concise factors of tensors. This is achieved through the application of multiple Convolutional Neural Networks (CNNs) in a non-linear manner along all possible dimensions with no assumed a priori knowledge. HB-DFL leverages the Hilbert basis tensor to enhance the stability of the solution by regularizing the core tensor to allow any component in a certain domain to interact with any component in the other dimensions. The final multi-domain features are handled through another multi-branch CNN to achieve reliable classification, exemplified here using MRI discrimination as a typical case. A case study of MRI discrimination has been performed on public MRI datasets for discrimination of PD and ADHD. Results indicate that 1) HB-DFL outperforms the counterparts in terms of FIT, mSIR and stability (mSC and umSC) of factor learning; 2) HB-DFL identifies PD and ADHD with an accuracy significantly higher than state-of-the-art methods do. Overall, HB-DFL has significant potentials for neuroimaging data analysis applications with its stability of automatic construction of structural features.
神经影像学数据（如磁共振成像、结构和功能磁共振成像）的分析在监测大脑动态和探测大脑结构方面发挥着重要作用。神经影像学数据本质上具有多特征和非线性，在执行诸如帕金森病（PD）和注意缺陷多动障碍（ADHD）等神经疾病的分类等自动分析之前，将这些数据组织为张量是一种自然的方式。然而，现有的方法往往受到性能瓶颈的限制（例如，传统的特征提取和基于深度学习的特征构建），因为这些方法可能会丢失与多个数据维度相关的结构信息，或者需要过多的经验和特定于应用的设置。本研究提出了一种基于希尔伯特基张量的深度因子学习模型（即 HB-DFL），用于自动推导出张量的潜在低维简洁因子。这是通过在所有可能的维度上以非线性方式应用多个卷积神经网络（CNN）来实现的，而无需假设先验知识。HB-DFL 利用希尔伯特基张量通过正则化核心张量来增强解的稳定性，允许某个域中的任何分量与其他维度中的任何分量相互作用。最后，通过另一个多分支 CNN 处理多域特征，以实现可靠的分类，这里以 MRI 分类为例。在公共 MRI 数据集上进行了 MRI 分类的案例研究，用于 PD 和 ADHD 的分类。结果表明：1）HB-DFL 在因子学习的 FIT、mSIR 和稳定性（mSC 和 umSC）方面优于对照组；2）HB-DFL 能够以显著高于现有方法的准确率识别 PD 和 ADHD。总体而言，HB-DFL 具有自动构建结构特征的稳定性，在神经影像学数据分析应用中具有重要潜力。
