Efficient cellular annotation of histopathology slides with real-time AI augmentation

In recent years, a steady swell of biological image data has driven rapid progress in healthcare applications of computer vision and machine learning. To make sense of this data, scientists often rely on detailed annotations from domain experts for training artificial intelligence (AI) algorithms. T...

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Autores principales:	James A. Diao, Richard J. Chen, Joseph C. Kvedar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/374430222596471db5c0a37994b36576
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spelling	oai:doaj.org-article:374430222596471db5c0a37994b365762021-11-28T12:07:13ZEfficient cellular annotation of histopathology slides with real-time AI augmentation10.1038/s41746-021-00534-02398-6352https://doaj.org/article/374430222596471db5c0a37994b365762021-11-01T00:00:00Zhttps://doi.org/10.1038/s41746-021-00534-0https://doaj.org/toc/2398-6352In recent years, a steady swell of biological image data has driven rapid progress in healthcare applications of computer vision and machine learning. To make sense of this data, scientists often rely on detailed annotations from domain experts for training artificial intelligence (AI) algorithms. The time-consuming and costly process of collecting annotations presents a sizable bottleneck for AI research and development. HALS (Human-Augmenting Labeling System) is a collaborative human-AI labeling workflow that uses an iterative “review-and-revise” model to improve the efficiency of this critical process in computational pathology.James A. DiaoRichard J. ChenJoseph C. KvedarNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 4, Iss 1, Pp 1-2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 James A. Diao Richard J. Chen Joseph C. Kvedar Efficient cellular annotation of histopathology slides with real-time AI augmentation
description	In recent years, a steady swell of biological image data has driven rapid progress in healthcare applications of computer vision and machine learning. To make sense of this data, scientists often rely on detailed annotations from domain experts for training artificial intelligence (AI) algorithms. The time-consuming and costly process of collecting annotations presents a sizable bottleneck for AI research and development. HALS (Human-Augmenting Labeling System) is a collaborative human-AI labeling workflow that uses an iterative “review-and-revise” model to improve the efficiency of this critical process in computational pathology.
format	article
author	James A. Diao Richard J. Chen Joseph C. Kvedar
author_facet	James A. Diao Richard J. Chen Joseph C. Kvedar
author_sort	James A. Diao
title	Efficient cellular annotation of histopathology slides with real-time AI augmentation
title_short	Efficient cellular annotation of histopathology slides with real-time AI augmentation
title_full	Efficient cellular annotation of histopathology slides with real-time AI augmentation
title_fullStr	Efficient cellular annotation of histopathology slides with real-time AI augmentation
title_full_unstemmed	Efficient cellular annotation of histopathology slides with real-time AI augmentation
title_sort	efficient cellular annotation of histopathology slides with real-time ai augmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/374430222596471db5c0a37994b36576
work_keys_str_mv	AT jamesadiao efficientcellularannotationofhistopathologyslideswithrealtimeaiaugmentation AT richardjchen efficientcellularannotationofhistopathologyslideswithrealtimeaiaugmentation AT josephckvedar efficientcellularannotationofhistopathologyslideswithrealtimeaiaugmentation
_version_	1718408192120061952

Efficient cellular annotation of histopathology slides with real-time AI augmentation

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