C
Cong Li
Researcher at Chinese Academy of Sciences
Publications - 10
Citations - 669
Cong Li is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Nomogram & Receiver operating characteristic. The author has an hindex of 4, co-authored 8 publications receiving 254 citations.
Papers
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Journal ArticleDOI
First-in-human liver-tumour surgery guided by multispectral fluorescence imaging in the visible and near-infrared-I/II windows
Zhenhua Hu,Cheng Fang,Bo Li,Zeyu Zhang,Zeyu Zhang,Caiguang Cao,Meishan Cai,Song Su,Xingwang Sun,Xiaojing Shi,Cong Li,Tie-Jun Zhou,Yuanxue Zhang,Chongwei Chi,Pan He,Xianming Xia,Yue Chen,Sanjiv S. Gambhir,Zhen Cheng,Jie Tian +19 more
TL;DR: It is inferred that combining the NIR-I/II spectral windows and suitable fluorescence probes might improve image-guided surgery in the clinic and help the fluorescence-guided surgical resection of liver tumours in patients.
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CT radiomics can help screen the Coronavirus disease 2019 (COVID-19): a preliminary study
Mengjie Fang,Bingxi He,Li Li,Di Dong,Xin Yang,Cong Li,Lingwei Meng,Lianzhen Zhong,Hailin Li,Hongjun Li,Jie Tian,Jie Tian +11 more
TL;DR: The experimental results suggest the radiomic signature could be a potential tool for diagnosis of the Coronavirus disease 2019, and the value of radiomics is investigated in screening COVID-19.
Journal ArticleDOI
Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics
Cong Li,Di Dong,Liang Li,Wei Gong,Xiaohu Li,Yan Bai,Meiyun Wang,Zhenhua Hu,Yunfei Zha,Jie Tian +9 more
TL;DR: A model combining radiomic and DL features of the lung could help distinguish critical cases from severe cases of COVID-19 and showed a strong correlation with patient outcomes.
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Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study
Hao Hu,Lixin Gong,Di Dong,Liang Zhu,Min Wang,Jie He,Lei Shu,Yiling Cai,Shi-Lun Cai,Wei Su,Yun-Shi Zhong,Cong Li,Yongbei Zhu,Mengjie Fang,Lianzhen Zhong,Xin Yang,Ping-Hong Zhou,Jie Tian,Jie Tian +18 more
TL;DR: A computer-aided diagnostic model based on VGG-19 with a single fully connected 2-classification layer exhibited comparable performance to senior endoscopists in the diagnosis of EGC and showed the potential value in aiding and improving the diagnosis.
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A deep learning-based radiomic nomogram for prognosis and treatment decision in advanced nasopharyngeal carcinoma: A multicentre study
Lianzhen Zhong,Di Dong,Xue-Liang Fang,Fan Zhang,Ning Zhang,Liwen Zhang,Mengjie Fang,Wei Jiang,Shaobo Liang,Cong Li,Yujia Liu,Xun Zhao,Runnan Cao,Hong Shan,Zhenhua Hu,Jun Ma,Ling-Long Tang,Jie Tian,Jie Tian +18 more
TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based model for treatment decision in locoregionally advanced nasopharyngeal carcinoma (NPC) patients, which could predict the prognosis of patients with different treatment regimens using multi-task deep learning radiomics and pre-treatment MR images.