Deep learning-based transformation of H&E stained tissues into special stains

Performing multiple histological stains on a biopsy can be costly and time consuming. Here the authors present a method for the digital transformation of H&E stained tissue into special stains (e.g., PAS, Masson’s Trichrome and Jones silver stain), and demonstrate that it improves diagnoses over...

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Autores principales:	Kevin de Haan, Yijie Zhang, Jonathan E. Zuckerman, Tairan Liu, Anthony E. Sisk, Miguel F. P. Diaz, Kuang-Yu Jen, Alexander Nobori, Sofia Liou, Sarah Zhang, Rana Riahi, Yair Rivenson, W. Dean Wallace, Aydogan Ozcan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/c775e79e449b4ff0bc20220df1cf8595
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spelling	oai:doaj.org-article:c775e79e449b4ff0bc20220df1cf85952021-12-02T15:07:56ZDeep learning-based transformation of H&E stained tissues into special stains10.1038/s41467-021-25221-22041-1723https://doaj.org/article/c775e79e449b4ff0bc20220df1cf85952021-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-25221-2https://doaj.org/toc/2041-1723Performing multiple histological stains on a biopsy can be costly and time consuming. Here the authors present a method for the digital transformation of H&E stained tissue into special stains (e.g., PAS, Masson’s Trichrome and Jones silver stain), and demonstrate that it improves diagnoses over the use of H&E only.Kevin de HaanYijie ZhangJonathan E. ZuckermanTairan LiuAnthony E. SiskMiguel F. P. DiazKuang-Yu JenAlexander NoboriSofia LiouSarah ZhangRana RiahiYair RivensonW. Dean WallaceAydogan OzcanNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kevin de Haan Yijie Zhang Jonathan E. Zuckerman Tairan Liu Anthony E. Sisk Miguel F. P. Diaz Kuang-Yu Jen Alexander Nobori Sofia Liou Sarah Zhang Rana Riahi Yair Rivenson W. Dean Wallace Aydogan Ozcan Deep learning-based transformation of H&E stained tissues into special stains
description	Performing multiple histological stains on a biopsy can be costly and time consuming. Here the authors present a method for the digital transformation of H&E stained tissue into special stains (e.g., PAS, Masson’s Trichrome and Jones silver stain), and demonstrate that it improves diagnoses over the use of H&E only.
format	article
author	Kevin de Haan Yijie Zhang Jonathan E. Zuckerman Tairan Liu Anthony E. Sisk Miguel F. P. Diaz Kuang-Yu Jen Alexander Nobori Sofia Liou Sarah Zhang Rana Riahi Yair Rivenson W. Dean Wallace Aydogan Ozcan
author_facet	Kevin de Haan Yijie Zhang Jonathan E. Zuckerman Tairan Liu Anthony E. Sisk Miguel F. P. Diaz Kuang-Yu Jen Alexander Nobori Sofia Liou Sarah Zhang Rana Riahi Yair Rivenson W. Dean Wallace Aydogan Ozcan
author_sort	Kevin de Haan
title	Deep learning-based transformation of H&E stained tissues into special stains
title_short	Deep learning-based transformation of H&E stained tissues into special stains
title_full	Deep learning-based transformation of H&E stained tissues into special stains
title_fullStr	Deep learning-based transformation of H&E stained tissues into special stains
title_full_unstemmed	Deep learning-based transformation of H&E stained tissues into special stains
title_sort	deep learning-based transformation of h&e stained tissues into special stains
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c775e79e449b4ff0bc20220df1cf8595
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Deep learning-based transformation of H&E stained tissues into special stains

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