Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

Convolutional neural networks have been applied to various areas of medical imaging and histology. Here the authors develop an automated approach using interpretable neural networks to determine Alzheimer’s disease plaque and cerebral amyloid angiopathy burden in post-mortem human brain tissue.

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Autores principales:	Ziqi Tang, Kangway V. Chuang, Charles DeCarli, Lee-Way Jin, Laurel Beckett, Michael J. Keiser, Brittany N. Dugger
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ba915e392b8548368af02fe9f5bed428
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spelling	oai:doaj.org-article:ba915e392b8548368af02fe9f5bed4282021-12-02T15:35:26ZInterpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline10.1038/s41467-019-10212-12041-1723https://doaj.org/article/ba915e392b8548368af02fe9f5bed4282019-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-10212-1https://doaj.org/toc/2041-1723Convolutional neural networks have been applied to various areas of medical imaging and histology. Here the authors develop an automated approach using interpretable neural networks to determine Alzheimer’s disease plaque and cerebral amyloid angiopathy burden in post-mortem human brain tissue.Ziqi TangKangway V. ChuangCharles DeCarliLee-Way JinLaurel BeckettMichael J. KeiserBrittany N. DuggerNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-14 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ziqi Tang Kangway V. Chuang Charles DeCarli Lee-Way Jin Laurel Beckett Michael J. Keiser Brittany N. Dugger Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline
description	Convolutional neural networks have been applied to various areas of medical imaging and histology. Here the authors develop an automated approach using interpretable neural networks to determine Alzheimer’s disease plaque and cerebral amyloid angiopathy burden in post-mortem human brain tissue.
format	article
author	Ziqi Tang Kangway V. Chuang Charles DeCarli Lee-Way Jin Laurel Beckett Michael J. Keiser Brittany N. Dugger
author_facet	Ziqi Tang Kangway V. Chuang Charles DeCarli Lee-Way Jin Laurel Beckett Michael J. Keiser Brittany N. Dugger
author_sort	Ziqi Tang
title	Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline
title_short	Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline
title_full	Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline
title_fullStr	Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline
title_full_unstemmed	Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline
title_sort	interpretable classification of alzheimer’s disease pathologies with a convolutional neural network pipeline
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/ba915e392b8548368af02fe9f5bed428
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Interpretable classification of Alzheimer’s disease pathologies with a convolutional neural network pipeline

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