Semi-Supervised Learning with Adaptive Pseudo-Label Selection and Correction for Predicting Overall Survival Time of Esophageal CancerOA
Semi-Supervised Learning with Adaptive Pseudo-Label Selection and Correction for Predicting Overall Survival Time of Esophageal Cancer
Hailin Yue;Hulin Kuang;Jin Liu;Junjian Li;Jie Zhu;Xiaoding Zhou;Pei Yang;Qifeng Wang;Jianxin Wang
Hunan Provincial Key Lab on Bioinformatics,School of Computer Science and Engineering,Central South University,Changsha 410083,ChinaHunan Provincial Key Lab on Bioinformatics,School of Computer Science and Engineering,Central South University,Changsha 410083,ChinaHunan Provincial Key Lab on Bioinformatics,School of Computer Science and Engineering,Central South University,Changsha 410083,ChinaHunan Provincial Key Lab on Bioinformatics,School of Computer Science and Engineering,Central South University,Changsha 410083,ChinaRadiation Oncology,Sichuan Cancer Center,Affiliated Cancer Hospital of University of Electronic Science and Technology of China,Chengdu 610000,ChinaRadiation Oncology,Sichuan Cancer Center,Affiliated Cancer Hospital of University of Electronic Science and Technology of China,Chengdu 610000,ChinaDepartment of Radiation Oncology,Hunan Cancer Hospital,Central South University,Changsha 410083,ChinaRadiation Oncology,Sichuan Cancer Center,Affiliated Cancer Hospital of University of Electronic Science and Technology of China,Chengdu 610000,ChinaHunan Provincial Key Lab on Bioinformatics,School of Computer Science and Engineering,Central South University,Changsha 410083,China
esophageal cancerOverall Survival time(OStime)semi-supervised learningcensored data
esophageal cancerOverall Survival time(OStime)semi-supervised learningcensored data
《大数据挖掘与分析（英文版）》 2026 (1)
295-313,19
This work was supported by the National Natural Science Foundation of China(No.U24A20256),the Radiation Oncology Key Laboratory of Sichuan Province Open Fund(No.2024ROKF02),the Natural Science Foundation of Hunan Province(No.2025JJ50374),and the Fundamental Research Funds for the Central Universities of Central South University(No.2024ZZTS0106).This work was carried out in part using computing resources at the High-Performance Computing Center of Central South University,China.
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