Author Correction: Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Xueyi Zheng, Zhao Yao, Yini Huang, Yanyan Yu, Yun Wang, Yubo Liu, Rushuang Mao, Fei Li, Yang Xiao, Yuanyuan Wang, Yixin Hu, Jinhua Yu () and Jianhua Zhou ()
Additional contact information
Xueyi Zheng: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine
Zhao Yao: Fudan University
Yini Huang: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine
Yanyan Yu: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Yun Wang: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine
Yubo Liu: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine
Rushuang Mao: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine
Fei Li: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine
Yang Xiao: Paul C. Lauterbur Research Center for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
Yuanyuan Wang: Fudan University
Yixin Hu: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine
Jinhua Yu: Fudan University
Jianhua Zhou: Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-1

Date: 2021
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DOI: 10.1038/s41467-021-24605-8

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