VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
Rattachement africain : us, gb. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
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 approaches. Our framework can easily be generalized to other classification tasks using different imaging modalities.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI
- Date Crossref
- 01/03/2022
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Harvard University pays non établi dans la noticeUniversité ou école supérieure
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Gordon Center for Medical Imaging pays non établi dans la noticeStructure de recherche
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Massachusetts General Hospital Department of Radiology pays non établi dans la noticeÉtablissement de santé
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Meta (United States) pays non établi dans la noticeEntreprise
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University of Southern California Department of Electrical and Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
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University of Sheffield Sheffield Institute for Translational Neuroscience pays non établi dans la noticeUniversité ou école supérieure
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Facebook AI pays non établi dans la noticeInstitution
Harvard University, Gordon Center for Medical Imaging et Department of Radiology — Massachusetts General Hospital, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.