A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

Pelvic radiographs (PXRs) are essential for detecting proximal femur and pelvis injuries in trauma patients, but none of the currently available algorithms can detect all kinds of trauma-related radiographic findings. Here, the authors develop a multiscale deep learning algorithm trained with weakly...

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Autores principales:	Chi-Tung Cheng, Yirui Wang, Huan-Wu Chen, Po-Meng Hsiao, Chun-Nan Yeh, Chi-Hsun Hsieh, Shun Miao, Jing Xiao, Chien-Hung Liao, Le Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/0cec1461b6754f12909e65f96ea806a5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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spelling	oai:doaj.org-article:0cec1461b6754f12909e65f96ea806a52021-12-02T10:52:47ZA scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs10.1038/s41467-021-21311-32041-1723https://doaj.org/article/0cec1461b6754f12909e65f96ea806a52021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21311-3https://doaj.org/toc/2041-1723Pelvic radiographs (PXRs) are essential for detecting proximal femur and pelvis injuries in trauma patients, but none of the currently available algorithms can detect all kinds of trauma-related radiographic findings. Here, the authors develop a multiscale deep learning algorithm trained with weakly supervised point annotation.Chi-Tung ChengYirui WangHuan-Wu ChenPo-Meng HsiaoChun-Nan YehChi-Hsun HsiehShun MiaoJing XiaoChien-Hung LiaoLe LuNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Chi-Tung Cheng Yirui Wang Huan-Wu Chen Po-Meng Hsiao Chun-Nan Yeh Chi-Hsun Hsieh Shun Miao Jing Xiao Chien-Hung Liao Le Lu A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs
description	Pelvic radiographs (PXRs) are essential for detecting proximal femur and pelvis injuries in trauma patients, but none of the currently available algorithms can detect all kinds of trauma-related radiographic findings. Here, the authors develop a multiscale deep learning algorithm trained with weakly supervised point annotation.
format	article
author	Chi-Tung Cheng Yirui Wang Huan-Wu Chen Po-Meng Hsiao Chun-Nan Yeh Chi-Hsun Hsieh Shun Miao Jing Xiao Chien-Hung Liao Le Lu
author_facet	Chi-Tung Cheng Yirui Wang Huan-Wu Chen Po-Meng Hsiao Chun-Nan Yeh Chi-Hsun Hsieh Shun Miao Jing Xiao Chien-Hung Liao Le Lu
author_sort	Chi-Tung Cheng
title	A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs
title_short	A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs
title_full	A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs
title_fullStr	A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs
title_full_unstemmed	A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs
title_sort	scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0cec1461b6754f12909e65f96ea806a5
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A scalable physician-level deep learning algorithm detects universal trauma on pelvic radiographs

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