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Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yin, P Imms, M Cheng, A Amgalan… - Proceedings of the …, 2023 - National Acad Sciences
The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …
被引用次数：46 相关文章

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.

C Yin, P Imms, M Cheng, A Amgalan… - Proceedings of the …, 2023 - europepmc.org
Results Neuroanatomic Patterns of Aging. We use an interpretable 3DCNN framework to
estimate the BAs of 650 CN adults (age range: 18 to 88 y; 325 males) from the Cambridge …
[PDF] usc.edu

[PDF][PDF] Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yina, P Immsb, M Chenga, A Amgalanb… - 2023 - gero.usc.edu
Results Neuroanatomic Patterns of Aging. We use an interpretable 3DCNN framework to
estimate the BAs of 650 CN adults (age range: 18 to 88 y; 325 males) from the Cambridge …

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yin, P Imms, M Cheng, A Amgalan… - Proceedings of the …, 2023 - pubmed.ncbi.nlm.nih.gov
The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yin, P Imms, M Cheng, A Amgalan… - Proceedings of the …, 2023 - ui.adsabs.harvard.edu
The phenotypic age of the human brain, as revealed via deep learning of anatomic magnetic
resonance images, reflects patterns of structural change related to cognitive decline. Our …
[HTML] nih.gov

[HTML][HTML] Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yin, P Imms, M Cheng, A Amgalan… - Proceedings of the …, 2023 - ncbi.nlm.nih.gov
Results Neuroanatomic Patterns of Aging. We use an interpretable 3DCNN framework to
estimate the BAs of 650 CN adults (age range: 18 to 88 y; 325 males) from the Cambridge …

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.

C Yin, P Imms, M Cheng, A Amgalan… - PNAS Proceedings of …, 2023 - psycnet.apa.org
The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yin, P Imms, M Cheng, A Amgalan… - Proceedings of the …, 2023 - par.nsf.gov
The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …
[PDF] semanticscholar.org

[PDF][PDF] Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yina, P Immsb, M Chenga, A Amgalanb… - 2023 - pdfs.semanticscholar.org
Results Neuroanatomic Patterns of Aging. We use an interpretable 3DCNN framework to
estimate the BAs of 650 CN adults (age range: 18 to 88 y; 325 males) from the Cambridge …

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment

C Yin, RA Sperling, P Imms, KA Johnson… - Proceedings of the …, 2023 - iro.uiowa.edu
The gap between chronological age (CA) and biological brain age, as estimated from
magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging …