A deep survival analysis method based on ranking. - Bing-Zhong Jing; Tao Zhang 060; Zixian Wang; Ying Jin; Kuiyuan Liu; Wenze Qiu; Liangru Ke; Ying Sun; Caisheng He; Dan Hou; Linquan Tang; Xing Lv; et al. (Artif. Intell. Medicine, 2019)

Deep learning for risk prediction in patients with nasopharyngeal carcinoma using multi-parametric MRIs. - Bing-Zhong Jing; Yishu Deng; Tao Zhang 060; Dan Hou; Bin Li; Mengyun Qiang; Kuiyuan Liu; Liangru Ke; Taihe Li; Ying Sun; Xing Lv; Chao-Feng Li (Comput. Methods Programs Biomed., 2020)

The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic area. - Yishu Deng; Chaofeng Li; Xing Lv; Weixiong Xia; Lujun Shen; Bing-Zhong Jing; Bin Li; Xiang Guo; Ying Sun; Chuanmiao Xie; Liangru Ke (Comput. Methods Programs Biomed., 2022)

SurvivalPath:A R package for conducting personalized survival path mapping based on time-series survival data. - Lujun Shen; Jinqing Mo; Changsheng Yang; Yiquan Jiang; Liangru Ke; Dan Hou; Jingdong Yan; Tao Zhang 060; Weijun Fan (PLoS Comput. Biol., 2023)