Annotation-efficient deep learning for automatic medical image segmentation

Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.

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Autores principales:	Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, Xin Liu, Jie Chen, Huihui Zhou, Ismail Ben Ayed, Hairong Zheng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/89468834a479418fa700e90078bef195
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spelling	oai:doaj.org-article:89468834a479418fa700e90078bef1952021-12-02T18:37:28ZAnnotation-efficient deep learning for automatic medical image segmentation10.1038/s41467-021-26216-92041-1723https://doaj.org/article/89468834a479418fa700e90078bef1952021-10-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26216-9https://doaj.org/toc/2041-1723Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.Shanshan WangCheng LiRongpin WangZaiyi LiuMeiyun WangHongna TanYaping WuXinfeng LiuHui SunRui YangXin LiuJie ChenHuihui ZhouIsmail Ben AyedHairong ZhengNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Shanshan Wang Cheng Li Rongpin Wang Zaiyi Liu Meiyun Wang Hongna Tan Yaping Wu Xinfeng Liu Hui Sun Rui Yang Xin Liu Jie Chen Huihui Zhou Ismail Ben Ayed Hairong Zheng Annotation-efficient deep learning for automatic medical image segmentation
description	Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obtain in many clinical applications. Here, the authors introduce an open-source framework to handle imperfect training datasets.
format	article
author	Shanshan Wang Cheng Li Rongpin Wang Zaiyi Liu Meiyun Wang Hongna Tan Yaping Wu Xinfeng Liu Hui Sun Rui Yang Xin Liu Jie Chen Huihui Zhou Ismail Ben Ayed Hairong Zheng
author_facet	Shanshan Wang Cheng Li Rongpin Wang Zaiyi Liu Meiyun Wang Hongna Tan Yaping Wu Xinfeng Liu Hui Sun Rui Yang Xin Liu Jie Chen Huihui Zhou Ismail Ben Ayed Hairong Zheng
author_sort	Shanshan Wang
title	Annotation-efficient deep learning for automatic medical image segmentation
title_short	Annotation-efficient deep learning for automatic medical image segmentation
title_full	Annotation-efficient deep learning for automatic medical image segmentation
title_fullStr	Annotation-efficient deep learning for automatic medical image segmentation
title_full_unstemmed	Annotation-efficient deep learning for automatic medical image segmentation
title_sort	annotation-efficient deep learning for automatic medical image segmentation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/89468834a479418fa700e90078bef195
work_keys_str_mv	AT shanshanwang annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT chengli annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT rongpinwang annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT zaiyiliu annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT meiyunwang annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT hongnatan annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT yapingwu annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT xinfengliu annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT huisun annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT ruiyang annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT xinliu annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT jiechen annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT huihuizhou annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT ismailbenayed annotationefficientdeeplearningforautomaticmedicalimagesegmentation AT hairongzheng annotationefficientdeeplearningforautomaticmedicalimagesegmentation
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Annotation-efficient deep learning for automatic medical image segmentation

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