VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI.
VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI.
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DOI：
10.1109/jbhi.2021.3097735
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发表时间：
2022-03
影响因子：
7.7
通讯作者：
Woo J
中科院分区：
文献类型：
--
作者：
Liu X;Xing F;Yang C;Kuo CJ;Babu S;Fakhri GE;Jenkins T;Woo J
深度学习在利用医学成像数据准确检测和分类疾病方面具有巨大的潜力，但其性能往往受到训练数据集数量和内存要求的限制。此外，许多深度学习模型被认为是一个“黑盒子”，从而往往限制了它们在临床应用中的采用。为了解决这个问题，我们提出了一个连续的子空间学习模型，称为VoxelHop，使用T2加权结构MRI数据对肌萎缩侧索硬化症（ALS）进行准确分类。与流行的卷积神经网络（CNN）架构相比，VoxelHop具有模块化和透明的结构，参数较少，没有任何反向传播，因此非常适合小数据集大小和3D成像数据。我们的VoxelHop有四个关键组成部分，包括（1）多通道3D数据的近到远邻域的顺序扩展;（2）用于无监督降维的子空间近似;（3）用于监督降维的标签辅助回归;以及（4）控制和患者之间的特征和分类的串联。我们的实验结果表明，我们的框架使用总共20个对照和26个患者，在区分患者和对照方面达到了93.48%的准确率和0.9394的AUC得分，即使使用相对较少的数据集，也显示了其鲁棒性和有效性。我们的全面评估也表明了它的有效性和优越性，最先进的3D CNN分类方法。我们的框架可以很容易地推广到其他分类任务，使用不同的成像方式。
Deep learning has great potential for accurate detection and classification of diseases with medical imaging data, but the performance is often limited by the number of training datasets and memory requirements. In addition, many deep learning models are considered a “black-box,” thereby often limiting their adoption in clinical applications. To address this, we present a successive subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) using T2-weighted structural MRI data. Compared with popular convolutional neural network (CNN) architectures, VoxelHop has modular and transparent structures with fewer parameters without any backpropagation, so it is well-suited to small dataset size and 3D imaging data. Our VoxelHop has four key components, including (1) sequential expansion of near-to-far neighborhood for multi-channel 3D data; (2) subspace approximation for unsupervised dimension reduction; (3) label-assisted regression for supervised dimension reduction; and (4) concatenation of features and classification between controls and patients. Our experimental results demonstrate that our framework using a total of 20 controls and 26 patients achieves an accuracy of 93.48% and an AUC score of 0.9394 in differentiating patients from controls, even with a relatively small number of datasets, showing its robustness and effectiveness. Our thorough evaluations also show its validity and superiority to the state-ofthe-art 3D CNN classification approaches. Our framework can easily be generalized to other classification tasks using different imaging modalities.