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000294310 1001_ $$aMa, Jun$$b0
000294310 245__ $$aUnleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge.
000294310 260__ $$aLondon$$bThe Lancet$$c2024
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000294310 520__ $$aDeep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To address these limitations, we organised the FLARE 2022 challenge to benchmark fast, low-resource, and accurate abdominal organ segmentation algorithms. We first constructed an intercontinental abdomen CT dataset from more than 50 clinical research groups. We then independently validated that deep learning algorithms achieved a median dice similarity coefficient (DSC) of 90·0% (IQR 87·4-91·3%) by use of 50 labelled images and 2000 unlabelled images, which can substantially reduce manual annotation costs. The best-performing algorithms successfully generalised to holdout external validation sets, achieving a median DSC of 89·4% (85·2-91·3%), 90·0% (84·3-93·0%), and 88·5% (80·9-91·9%) on North American, European, and Asian cohorts, respectively. These algorithms show the potential to use unlabelled data to boost performance and alleviate annotation shortages for modern artificial intelligence models.
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000294310 650_2 $$2MeSH$$aDeep Learning
000294310 650_2 $$2MeSH$$aHumans
000294310 650_2 $$2MeSH$$aTomography, X-Ray Computed
000294310 650_2 $$2MeSH$$aAlgorithms
000294310 650_2 $$2MeSH$$aAbdomen: diagnostic imaging
000294310 7001_ $$aZhang, Yao$$b1
000294310 7001_ $$aGu, Song$$b2
000294310 7001_ $$aGe, Cheng$$b3
000294310 7001_ $$aMae, Shihao$$b4
000294310 7001_ $$aYoung, Adamo$$b5
000294310 7001_ $$aZhu, Cheng$$b6
000294310 7001_ $$aYang, Xin$$b7
000294310 7001_ $$aMeng, Kangkang$$b8
000294310 7001_ $$aHuang, Ziyan$$b9
000294310 7001_ $$aZhang, Fan$$b10
000294310 7001_ $$aPan, Yuanke$$b11
000294310 7001_ $$aHuang, Shoujin$$b12
000294310 7001_ $$aWang, Jiacheng$$b13
000294310 7001_ $$aSun, Mingze$$b14
000294310 7001_ $$aZhang, Rongguo$$b15
000294310 7001_ $$aJia, Dengqiang$$b16
000294310 7001_ $$aChoi, Jae Won$$b17
000294310 7001_ $$aAlves, Natália$$b18
000294310 7001_ $$ade Wilde, Bram$$b19
000294310 7001_ $$0P:(DE-HGF)0$$aKoehler, Gregor$$b20
000294310 7001_ $$aLai, Haoran$$b21
000294310 7001_ $$aWang, Ershuai$$b22
000294310 7001_ $$0P:(DE-He78)1042737c83ba70ec508bdd99f0096864$$aWiesenfarth, Manuel$$b23
000294310 7001_ $$aZhu, Qiongjie$$b24
000294310 7001_ $$aDong, Guoqiang$$b25
000294310 7001_ $$aHe, Jian$$b26
000294310 7001_ $$aConsortium, FLARE Challenge$$b27$$eCollaboration Author
000294310 7001_ $$aWang, Bo$$b28
000294310 7001_ $$aHe, Junjun$$b29$$eContributor
000294310 7001_ $$aYang, Hua$$b30$$eContributor
000294310 7001_ $$aHuang, Bingding$$b31$$eContributor
000294310 7001_ $$aLyu, Mengye$$b32$$eContributor
000294310 7001_ $$aMa, Yongkang$$b33$$eContributor
000294310 7001_ $$aGuo, Heng$$b34$$eContributor
000294310 7001_ $$aXu, Weixin$$b35$$eContributor
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000294310 7001_ $$aWu, Yajun$$b37$$eContributor
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