Robust coupled tensor decomposition and feature extraction for multimodal medical data.
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| Title: | Robust coupled tensor decomposition and feature extraction for multimodal medical data. |
|---|---|
| Authors: | Zhao, Meng1 (AUTHOR), Reisi Gahrooei, Mostafa1 (AUTHOR), Gaw, Nathan2 (AUTHOR) |
| Source: | IISE Transactions on Healthcare Systems Engineering. Apr-Jun2023, Vol. 13 Issue 2, p117-131. 15p. |
| Subjects: | Feature extraction, Functional magnetic resonance imaging, Touch screens, People with cerebral palsy |
| Abstract: | High-dimensional and multimodal data to describe various aspects of a patient's clinical condition have become increasingly abundant in the medical field across a variety of domains. For example, in neuroimaging applications, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be collected simultaneously (i.e., EEG-fMRI) to provide high spatial and temporal resolution of a patient's brain function. Additionally, in telemonitoring applications, a smartphone can be used to record various aspects of a patient's condition using its built-in microphone, accelerometer, touch screen, etc. Coupled CANDECOMP/PARAFAC decomposition (CCPD) is a powerful approach to simultaneously extract common structures and features from multiple tensors and can be applied to these high-dimensional, multi-modal data. However, the existing CCPD models are inadequate to handle outliers, which are highly present in both applications. For EEG-fMRI, outliers are common due to fluctuations in the electromagnetic field resulting from interference between the EEG electrodes and the fMRI machine. For telemonitoring, outliers can result from patients not properly following instructions while performing smartphone-guided exercises at home. This motivates us to propose a robust CCPD (RCCPD) method for robust feature extraction. The proposed method utilizes the Alternating Direction Method of Multipliers (ADMM) to minimize an objective function that simultaneously decomposes a pair of coupled tensors and isolates outliers. We compare the proposed RCCPD method with the classical CP decomposition, the coupled matrix-tensor/tensor-tensor factorization (CMTF/CTTF), and the tensor robust CP decomposition (TRCPD). Experiments on both synthetic and real-world data demonstrate that the proposed RCCPD effectively handles outliers and outperforms the benchmarks in terms of accuracy. [ABSTRACT FROM AUTHOR] |
| Copyright of IISE Transactions on Healthcare Systems Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 163915566 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust coupled tensor decomposition and feature extraction for multimodal medical data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Meng%22">Zhao, Meng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reisi+Gahrooei%2C+Mostafa%22">Reisi Gahrooei, Mostafa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gaw%2C+Nathan%22">Gaw, Nathan</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IISE+Transactions+on+Healthcare+Systems+Engineering%22">IISE Transactions on Healthcare Systems Engineering</searchLink>. Apr-Jun2023, Vol. 13 Issue 2, p117-131. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+magnetic+resonance+imaging%22">Functional magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Touch+screens%22">Touch screens</searchLink><br /><searchLink fieldCode="DE" term="%22People+with+cerebral+palsy%22">People with cerebral palsy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: High-dimensional and multimodal data to describe various aspects of a patient's clinical condition have become increasingly abundant in the medical field across a variety of domains. For example, in neuroimaging applications, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be collected simultaneously (i.e., EEG-fMRI) to provide high spatial and temporal resolution of a patient's brain function. Additionally, in telemonitoring applications, a smartphone can be used to record various aspects of a patient's condition using its built-in microphone, accelerometer, touch screen, etc. Coupled CANDECOMP/PARAFAC decomposition (CCPD) is a powerful approach to simultaneously extract common structures and features from multiple tensors and can be applied to these high-dimensional, multi-modal data. However, the existing CCPD models are inadequate to handle outliers, which are highly present in both applications. For EEG-fMRI, outliers are common due to fluctuations in the electromagnetic field resulting from interference between the EEG electrodes and the fMRI machine. For telemonitoring, outliers can result from patients not properly following instructions while performing smartphone-guided exercises at home. This motivates us to propose a robust CCPD (RCCPD) method for robust feature extraction. The proposed method utilizes the Alternating Direction Method of Multipliers (ADMM) to minimize an objective function that simultaneously decomposes a pair of coupled tensors and isolates outliers. We compare the proposed RCCPD method with the classical CP decomposition, the coupled matrix-tensor/tensor-tensor factorization (CMTF/CTTF), and the tensor robust CP decomposition (TRCPD). Experiments on both synthetic and real-world data demonstrate that the proposed RCCPD effectively handles outliers and outperforms the benchmarks in terms of accuracy. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IISE Transactions on Healthcare Systems Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/24725579.2022.2141929 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 117 Subjects: – SubjectFull: Feature extraction Type: general – SubjectFull: Functional magnetic resonance imaging Type: general – SubjectFull: Touch screens Type: general – SubjectFull: People with cerebral palsy Type: general Titles: – TitleFull: Robust coupled tensor decomposition and feature extraction for multimodal medical data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Meng – PersonEntity: Name: NameFull: Reisi Gahrooei, Mostafa – PersonEntity: Name: NameFull: Gaw, Nathan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr-Jun2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 24725579 Numbering: – Type: volume Value: 13 – Type: issue Value: 2 Titles: – TitleFull: IISE Transactions on Healthcare Systems Engineering Type: main |
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