Ensemble deep learning for Alzheimer’s disease characterization and estimation

Tanveer, M; Goel, T; Sharma, R; Malik, AK; Beheshti, I; Del Ser, J; Suganthan, PN; Lin, CT

Ensemble deep learning for Alzheimer’s disease characterization and estimation

Tanveer, M Goel, T Sharma, R Malik, AK Beheshti, I Del Ser, J Suganthan, PN Lin, CT

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Publisher:: SPRINGERNATURE
Publication Type:: Journal Article
Citation:: Nature Mental Health, 2024, 2, (6), pp. 655-667
Issue Date:: 2024-06-01

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Field	Value	Language
dc.contributor.author	Tanveer, M
dc.contributor.author	Goel, T
dc.contributor.author	Sharma, R
dc.contributor.author	Malik, AK
dc.contributor.author	Beheshti, I
dc.contributor.author	Del Ser, J
dc.contributor.author	Suganthan, PN
dc.contributor.author	Lin, CT
dc.date.accessioned	2025-03-10T04:05:46Z
dc.date.available	2025-03-10T04:05:46Z
dc.date.issued	2024-06-01
dc.identifier.citation	Nature Mental Health, 2024, 2, (6), pp. 655-667
dc.identifier.issn	2731-6076
dc.identifier.issn	2731-6076
dc.identifier.uri	http://hdl.handle.net/10453/185618
dc.description.abstract	Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by Alzheimer’s disease in the brain. Diagnosing Alzheimer’s disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer’s disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer’s disease diagnosis. These models combine several deep neural networks to increase a prediction’s robustness. Here we revisit key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess where our knowledge in this area stands.
dc.language	English
dc.publisher	SPRINGERNATURE
dc.relation	http://purl.org/au-research/grants/arc/DP220100803
dc.relation	http://purl.org/au-research/grants/nhmrc/APP2021183
dc.relation.ispartof	Nature Mental Health
dc.relation.isbasedon	10.1038/s44220-024-00237-x
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections.
dc.title	Ensemble deep learning for Alzheimer’s disease characterization and estimation
dc.type	Journal Article
utslib.citation.volume	2
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Stroke Research Collaborative
utslib.copyright.status	open_access	*
dc.date.updated	2025-03-10T04:05:44Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	2
utslib.citation.issue	6

Abstract:

Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by Alzheimer’s disease in the brain. Diagnosing Alzheimer’s disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer’s disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer’s disease diagnosis. These models combine several deep neural networks to increase a prediction’s robustness. Here we revisit key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess where our knowledge in this area stands.

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http://hdl.handle.net/10453/185618