个人简介
教育工作背景
2021.12起 北京航空航天大学，医学科学与工程学院，副教授
2019.7-2021.11 北京航空航天大学，医工交叉创新研究院，博士后
2014.9-2019.7 中国科学院自动化研究所，模式识别与智能系统，工学博士
2010.9-2014.7 电子科技大学， 自动化，工学学士
主要从事医学影像人工智能、深度学习、医学图像分割、识别、智能计算等方向，研发新的深度学习算法进行医学影像分析，实现对肺癌等重大疾病的精准诊断、疗效预测、精准医疗；相关成果作为第一作者发表在柳叶刀子刊Lancet Digital Health (IF: 36.615)，呼吸医学顶刊European Respiratory Journal (IF: 33.809, 2篇, 均为ESI高被引论文)、JAMA子刊JAMA Network Open (IF: 13.366)和医学图像分析顶刊Medical Image Analysis (IF: 13.828, ESI高被引论文)上。5篇文章入选ESI高被引论文，近5年谷歌学术被引3600余次，H-index: 22。
入选中国科协“青年人才托举工程”，主持国家自然科学基金-重大研究计划（培育）、青年基金等项目。担任Lancet, Nature Communications, IEEE Transactions on Medical Imaging等期刊的审稿人。
代表性科研项目
中国科协“青年人才托举工程”，2022-2024，主持
国家自然科学基金，重大研究计划（培育），基于影像-病理多组学深度学习的晚期肺癌免疫治疗疗效预测，2023.01-2025.12，主持
国家自然科学基金青年基金，基于多任务深度学习和全肺影像分析的肺癌EGFR基因突变预测模型研究，2021.01-2023.12，主持
国家自然科学基金重点项目，基于多模态影像组学的肝细胞癌微血管侵犯预测关键问题研究，2020.01-2024.12，参与
近期论文
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Shuo Wang#, He Yu, Yuncui Gan, Zhangjie Wu, Encheng Li, Xiaohu Li, Jingxue Cao, Yongbei Zhu, Liusu Wang, Hui Deng, Mei Xie, Yuanyong Wang, Xidong Ma, Dan Liu, Bojiang Chen, Panwen Tian, Zhixin Qiu, Jinghong Xian, Jing Ren, Kun Wang, Wei Wei, Fei Xie*, Zhenhui Li*, Qi Wang*, Xinying Xue*, Zaiyi Liu*, Jingyun Shi*, Weimin Li*, Jie Tian*. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study [J]. Lancet Digital Health, 2022, 4(5): e309-e319,2022.
Shuo Wang#, Yunfei Zha#, Weimin Li#, Qingxia Wu#, Xiaohu Li#, Meng Niu#, Meiyun Wang#, Xiaoming Qiu#, Hongjun Li#, He Yu, Wei Gong, Yan Bai, Li Li, Yongbei Zhu, Liusu Wang, Jie Tian*. A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis [J]. European Respiratory Journal. 2020, 56 (2): 2000775. (ESI高被引)
Shuo Wang#, Jingyun Shi#, Zhaoxiang Ye#, Di Dong#, Dongdong Yu#, Mu Zhou#, Ying Liu, Olivier Gevaert, Kun Wang, Yongbei Zhu, Hongyu Zhou, Zhenyu Liu, Jie Tian*. Predicting EGFR Mutation Status in Lung Adenocarcinoma on Computed Tomography Image using Deep Learning [J]. European Respiratory Journal, 2019, 53(3): 1800986. (ESI高被引)
Shuo Wang#, Mu Zhou#, Zaiyi Liu, Zhenyu Liu, Dongsheng Gu, Yali Zang, Di Dong#, Olivier Gevaert#, Jie Tian*. Central Focused Convolutional Neural Networks: Developing a Data-driven Model for Lung Nodule Segmentation [J]. Medical Image Analysis, 40 (2017): 172-183. (ESI高被引)
Shuo Wang#, Zhenyu Liu#, Yu Rong#, Bin Zhou, Yan Bai, Wei Wei, Wei Wei, Meiyun Wang*, Yingkun Guo*, Jie Tian*. Deep Learning Provides a New Computed Tomography-based Prognostic Biomarker for Recurrence Prediction in High-grade Serous Ovarian Cancer [J]. Radiotherapy and Oncology, 2019, 132: 171-177.
Yongbei Zhu#, Shuo Wang#, Siwen Wang, Qingxia Wu, Liusu Wang, Hongjun Li, Meiyun Wang, Meng Niu, Yunfei Zha, Jie Tian*. Mix Contrast for COVID-19 Mild-to-Critical Prediction. IEEE Transactions on Biomedical Engineering, 2021, 68(12):3725-3736.
Qingxia Wu#, Shuo Wang#, Shuixing Zhang#, Meiyun Wang#, Yingying Ding#, Jin Fang, Qingxia Wu, Wei Qian, Zhenyu Liu, Kai Sun, Yan Jin, He Ma*, Jie Tian*. Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients with Cervical Cancer [J]. JAMA Network Open. 2020;3(7): e2011625.
Qingxia Wu#, Shuo Wang#, Xi Chen#, Yan Wang, Li Dong, Zhenyu Liu*, Jie Tian*, Meiyun Wang*. Radiomics Analysis of Magnetic Resonance Imaging Improves Diagnostic Performance of Lymph Node Metastasis in Patients with Cervical Cancer [J]. Radiotherapy and Oncology, 2019, 138: 141-148.
Qingxia Wu#, Shuo Wang#, Liang Li#, Qingxia Wu, Wei Qian, Yahua Hu, Li Li, Xuezhi Zhou, He Ma*, Hongjun Li*, Meiyun Wang*, Xiaoming Qiu*, Yunfei Zha*, Jie Tian*. Radiomics Analysis of Computed Tomography helps Predict Poor Prognostic Outcome in COVID-19 [J]. Theranostics, 2020; 10(16):7231-7244.
Zhenyu Liu#, Shuo Wang#, Di Dong#, Jingwei Wei#, Cheng Fang#, Xuezhi Zhou, Kai Sun, Longfei Li, Bo Li*, Meiyun Wang*, Jie Tian*. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges [J]. Theranostics, 2019, 9(5): 1303. (ESI高被引)
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