首页> Nature communications> Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

Robustly federated learning model for identifying high-risk patients with postoperative gastric cancer recurrence.

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第一作者： Bao,Feng

第一单位： Department of Radiology, Jiangmen Central Hospital, Jiangmen, China.;Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, China.

作者： Bao,Feng ^[1] ; Jiangfeng,Shi ^[2] ; Liebin,Huang ^[3] ; Zhiqi,Yang ^[4] ; Shi-Ting,Feng ^[5] ; Jianpeng,Li ^[6] ; Qinxian,Chen ^[3] ; Huimin,Xue ^[3] ; Xiangguang,Chen ^[4] ; Cuixia,Wan ^[4] ; Qinghui,Hu ^[7] ; Enming,Cui ^[3] ; Yehang,Chen ^[8] ; Wansheng,Long ^[9]

作者单位： Department of Radiology, Jiangmen Central Hospital, Jiangmen, China.;Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, China. ^[1] Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, China.;School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China. ^[2] Department of Radiology, Jiangmen Central Hospital, Jiangmen, China. ^[3] Department of Radiology, Meizhou People's Hospital, Meizhou, China. ^[4] Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. ^[5] Department of Radiology, Dongguan People's Hospital, Dongguan, China. ^[6] Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, China. ^[7] Laboratory of Intelligent Detection and Information Processing, Guilin University of Aerospace Technology, Guilin, China. cyh93yl@163.com. ^[8] Department of Radiology, Jiangmen Central Hospital, Jiangmen, China. jmlws2@163.com. ^[9]

医学主题词人类(Humans);胃肿瘤(Stomach Neoplasms);人工智能(Artificial Intelligence);学习(Learning);算法(Algorithms)

DOI 10.1038/s41467-024-44946-4

PMID 38272913

发布时间 2024-10-23

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