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A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.

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Title: A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.
Authors: Yao N; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China., Hu H; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China., Chen K; Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China., Huang H; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China., Zhao C; Department of Applied Computing, Michigan Technological University, Houghton, MI, USA.; Department of Computer Science, Kennesaw State University, Marietta, GA, USA., Guo Y; Department of Radiology, The First People's Hospital of Guangzhou, Guangzhou, 510180, Guangdong, China., Li B; Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China., Nan J; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China., Li Y; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China., Han C; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China., Zhu F; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China., Zhou W; Department of Applied Computing, Michigan Technological University, Houghton, MI, USA. whzhou@mtu.edu.; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA. whzhou@mtu.edu., Tian L; Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China. tianli@sysucc.org.cn.
Source: Journal of imaging informatics in medicine [J Imaging Inform Med] 2025 Jun; Vol. 38 (3), pp. 1323-1333. Date of Electronic Publication: 2024 Sep 23.
Publication Type: Journal Article
Journal Info: Publisher: Springer Nature Country of Publication: Switzerland NLM ID: 9918663679206676 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2948-2933 (Electronic) Linking ISSN: 29482925 NLM ISO Abbreviation: J Imaging Inform Med Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.
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  Data: <searchLink fieldCode="AU" term="%22Yao+N%22">Yao N</searchLink>; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.<br /><searchLink fieldCode="AU" term="%22Hu+H%22">Hu H</searchLink>; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.<br /><searchLink fieldCode="AU" term="%22Chen+K%22">Chen K</searchLink>; Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.<br /><searchLink fieldCode="AU" term="%22Huang+H%22">Huang H</searchLink>; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.<br /><searchLink fieldCode="AU" term="%22Zhao+C%22">Zhao C</searchLink>; Department of Applied Computing, Michigan Technological University, Houghton, MI, USA.; Department of Computer Science, Kennesaw State University, Marietta, GA, USA.<br /><searchLink fieldCode="AU" term="%22Guo+Y%22">Guo Y</searchLink>; Department of Radiology, The First People's Hospital of Guangzhou, Guangzhou, 510180, Guangdong, China.<br /><searchLink fieldCode="AU" term="%22Li+B%22">Li B</searchLink>; Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China.<br /><searchLink fieldCode="AU" term="%22Nan+J%22">Nan J</searchLink>; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.<br /><searchLink fieldCode="AU" term="%22Li+Y%22">Li Y</searchLink>; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.<br /><searchLink fieldCode="AU" term="%22Han+C%22">Han C</searchLink>; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.<br /><searchLink fieldCode="AU" term="%22Zhu+F%22">Zhu F</searchLink>; School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.<br /><searchLink fieldCode="AU" term="%22Zhou+W%22">Zhou W</searchLink>; Department of Applied Computing, Michigan Technological University, Houghton, MI, USA. whzhou@mtu.edu.; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA. whzhou@mtu.edu.<br /><searchLink fieldCode="AU" term="%22Tian+L%22">Tian L</searchLink>; Department of Medical Imaging, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, Guangdong, China. tianli@sysucc.org.cn.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Springer+Nature%22">Springer Nature </searchLink><i>Country of Publication: </i>Switzerland <i>NLM ID: </i>9918663679206676 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2948-2933 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2229482925%22">29482925 </searchLink><i>NLM ISO Abbreviation: </i>J Imaging Inform Med <i>Subsets: </i>MEDLINE
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        Value: 10.1007/s10278-024-01276-7
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              Text: 2025 Jun
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