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| Home > Publications database > Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge. > print |
| Home > Publications database > Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge. > print |
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| 005 | 20250210095039.0 | ||
| 024 | 7 | _ | |a 10.1016/S2589-7500(24)00154-7 |2 doi |
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| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Ma, Jun |b 0 |
| 245 | _ | _ | |a Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge. |
| 260 | _ | _ | |a London |c 2024 |b The Lancet |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Deep 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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| 650 | _ | 2 | |a Deep Learning |2 MeSH |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Tomography, X-Ray Computed |2 MeSH |
| 650 | _ | 2 | |a Algorithms |2 MeSH |
| 650 | _ | 2 | |a Abdomen: diagnostic imaging |2 MeSH |
| 700 | 1 | _ | |a Zhang, Yao |b 1 |
| 700 | 1 | _ | |a Gu, Song |b 2 |
| 700 | 1 | _ | |a Ge, Cheng |b 3 |
| 700 | 1 | _ | |a Mae, Shihao |b 4 |
| 700 | 1 | _ | |a Young, Adamo |b 5 |
| 700 | 1 | _ | |a Zhu, Cheng |b 6 |
| 700 | 1 | _ | |a Yang, Xin |b 7 |
| 700 | 1 | _ | |a Meng, Kangkang |b 8 |
| 700 | 1 | _ | |a Huang, Ziyan |b 9 |
| 700 | 1 | _ | |a Zhang, Fan |b 10 |
| 700 | 1 | _ | |a Pan, Yuanke |b 11 |
| 700 | 1 | _ | |a Huang, Shoujin |b 12 |
| 700 | 1 | _ | |a Wang, Jiacheng |b 13 |
| 700 | 1 | _ | |a Sun, Mingze |b 14 |
| 700 | 1 | _ | |a Zhang, Rongguo |b 15 |
| 700 | 1 | _ | |a Jia, Dengqiang |b 16 |
| 700 | 1 | _ | |a Choi, Jae Won |b 17 |
| 700 | 1 | _ | |a Alves, Natália |b 18 |
| 700 | 1 | _ | |a de Wilde, Bram |b 19 |
| 700 | 1 | _ | |a Koehler, Gregor |0 P:(DE-HGF)0 |b 20 |
| 700 | 1 | _ | |a Lai, Haoran |b 21 |
| 700 | 1 | _ | |a Wang, Ershuai |b 22 |
| 700 | 1 | _ | |a Wiesenfarth, Manuel |0 P:(DE-He78)1042737c83ba70ec508bdd99f0096864 |b 23 |
| 700 | 1 | _ | |a Zhu, Qiongjie |b 24 |
| 700 | 1 | _ | |a Dong, Guoqiang |b 25 |
| 700 | 1 | _ | |a He, Jian |b 26 |
| 700 | 1 | _ | |a Consortium, FLARE Challenge |b 27 |e Collaboration Author |
| 700 | 1 | _ | |a Wang, Bo |b 28 |
| 700 | 1 | _ | |a He, Junjun |b 29 |e Contributor |
| 700 | 1 | _ | |a Yang, Hua |b 30 |e Contributor |
| 700 | 1 | _ | |a Huang, Bingding |b 31 |e Contributor |
| 700 | 1 | _ | |a Lyu, Mengye |b 32 |e Contributor |
| 700 | 1 | _ | |a Ma, Yongkang |b 33 |e Contributor |
| 700 | 1 | _ | |a Guo, Heng |b 34 |e Contributor |
| 700 | 1 | _ | |a Xu, Weixin |b 35 |e Contributor |
| 700 | 1 | _ | |a Maier-Hein, Klaus |0 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |b 36 |u dkfz |
| 700 | 1 | _ | |a Wu, Yajun |b 37 |e Contributor |
| 773 | _ | _ | |a 10.1016/S2589-7500(24)00154-7 |g Vol. 6, no. 11, p. e815 - e826 |0 PERI:(DE-600)2972368-1 |n 11 |p e815 - e826 |t The lancet / Digital health |v 6 |y 2024 |x 2589-7500 |
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