A deep learning framework identifies dimensional representations of Alzheimer’s Disease from brain structure
Alzheimer’s disease is heterogeneous in its neuroimaging and clinical phenotypes. Here the authors present a semi-supervised deep learning method, Smile-GAN, to show four neurodegenerative patterns and two progression pathways providing prognostic and clinical information.
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Autores principales: | Zhijian Yang, Ilya M. Nasrallah, Haochang Shou, Junhao Wen, Jimit Doshi, Mohamad Habes, Guray Erus, Ahmed Abdulkadir, Susan M. Resnick, Marilyn S. Albert, Paul Maruff, Jurgen Fripp, John C. Morris, David A. Wolk, Christos Davatzikos, iSTAGING Consortium, Baltimore Longitudinal Study of Aging (BLSA), Alzheimer’s Disease Neuroimaging Initiative (ADNI) |
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Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
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Materias: | |
Acceso en línea: | https://doaj.org/article/bc622eef4986462d950294c8155877a6 |
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