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PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification
A new machine learning methodology, called successive subspace learning ...
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Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls
Convolutional Neural Network (CNN) has been successfully applied on clas...
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Using Dimension Reduction to Improve the Classification of High-dimensional Data
In this work we show that the classification performance of high-dimensi...
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Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
Automated methods for Alzheimer's disease (AD) classification have the p...
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A Deep Dive into Understanding Tumor Foci Classification using Multiparametric MRI Based on Convolutional Neural Network
Data scarcity has refrained deep learning models from making greater pro...
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Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks
Deep learning models for MRI classification face two recurring problems:...
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G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification
We propose a novel deep neural network architecture to integrate imaging...
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VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
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-of-the-art 3D CNN classification methods. Our framework can easily be generalized to other classification tasks using different imaging modalities.
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