Resume
Dr. Chen Ying received a bachelor's degree in circuit and system from Nanjing University in 1998 and a doctor's degree in acoustics in 2003. His main research focuses on biomedical signal processing and image processing. From 2005 to 2006, he received funding from the French China Application Foundation (FFCSA) and worked as a postdoctoral at the French higher electric power academy (superelec) for one year. In 2015, Dr. Chen Ying received a grant from the national study abroad foundation and went to Harvard Medical School to work as a visiting scholar for one year. His current research interests include medical signal processing, image processing, deep learning and so on. His papers cover many fields such as medicine and image deep learning.
Research Fields
Biomedical signal processing, image processing, deep learning
Publications
1.Chen Ying, Ning xinbao, Wang Zhenzhou, Xue longzeng, Wang Yelin, etc. Dynamical Correlation Dimension Analysis of Heart Electric Activities. Journal of Nanjing University (Natural Sciences) 2002; 38(1):20~28. 2.Chen Ying, Ning Xinbao, Wang Jun. Dynamical Analysis of the Nonlinearity in the Model of Acute Myocardial Ischemia. Journal of biomedical engineering 2004; 21(5):770-774. 3.Chen Ying, Ning Xinbao, Wang Jun. Dynamical correlation dimension of acute myocardial infarction, Chinese Journal of Biomedical Engineering 2005; 24(3): 381-384. 4.Chen, Y.; Nash, M.P.; Ning, X.; Wang, Y.; Paterson, D.J.; Wang, J., The SARD variety of multifractality of ventricular epicardial mapping during ischemia, Chinese Science Bulletin 2006; 51( 7) : 809 – 814. 5.Chen, Y.; Nash, M.P.; Ning, X.; Wang, Y.; Paterson, D.J.; Wang, J., Multifractal ECG Mapping of Ventricular Epicardium During Regional Ischemia in the Pig, IEEE Transactions on Biomedical Engineering 2006; 53(10): 1920 – 1925. 6.Ying Chen, Zhong Wu, Cuiwei Yang, Jun Shao, Kelvin Kian Loong Wong, Derek Abbott. Investigation of Atrial Vulnerability by Analysis of the Sinus Node EG From Atrial Fibrillation Models Using a Phase Synchronization Method. IEEE Transactions on Biomedical Engineering 2012; 59(9): 2668-2676. 7.Yudan Huang, Ying Chen, Haochuan Zhu, Weifeng Li, Yun Ge, Xiaolin Huang, Jian He,A liver fibrosis staging method using cross-contrast network,Expert Systems with Applications 2019; 130: 124-131. 8.Yunzhong Wang, Ziying Xu, Siyue Bai, Qiyuan Wang, Ying Chen, Weifeng Li, Xiaoling Huang & Yun Ge (2020) Intelligent painting identification based on image perception in multimedia enterprise, Enterprise Information Systems, DOI: 10.1080/17517575.2020.1755456 9.Qiyuan Wang, Zhongmin Wang, Yu Sun, Xin Zhang, Weifeng Li, Yun Ge, Xiaolin Huang, Yun Liu, Ying Chen. SCCNN: A Diagnosis Method for Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Siamese Cross Contrast Neural Network. IEEE Access. 2020. vol. 8, pp. 85271-85283. 10、Hu Z, Cao D, Hu Y, Wang B, Zhang Y, Tang R, Zhuang J, Gao A, Chen Y, Lin Z. Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images. BMC Oral Health. 2022 Sep 5;22(1):382. doi: 10.1186/s12903-022-02422-9. PMID: 36064682; PMCID: PMC9446797.https://pubmed.ncbi.nlm.nih.gov/36064682/
11、 Hu Z, Wang B, Pan X, Cao D, Gao A, Yang X, Chen Y and Lin Z (2022) Using deep learning to distinguish malignant from benign parotid tumors on plain computed tomography images. Front. Oncol. 12:919088. doi: 10.3389/fonc.2022.919088。https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.919088/full
12、Tong Wu, En-Xiang Shen, Zhi-Bin Jin, Yong Jiang, Ying Chen, Chao Tao, Jie Yuan, Yun Ge, Xiao-Jun Liu, Characterization of anisotropy of elastic modulus with three-dimensional freehand scan shear wave elasticity imaging, J. Med. Imag. 10(6) 066002 (17 November 2023) .https://doi.org/10.1117/1.JMI.10.6.066002 13、Weiyu Sun, Xinyu Zhang, Hao Lu, Ying Chen, Yun Ge, Xiaolin Huang, Jie Yuan, and Yingcong Chen. 2023. Resolve Domain Conflicts for Generalizable Remote Physiological Measurement. In Proceedings of the 31st ACM International Conference on Multimedia (MM '23). Association for Computing Machinery, New York, NY, USA, 8214–8224. https://doi.org/10.1145/3581783.3612265 14、Sun, Y., Wang, Y., Gan, K. et al. Reliable Delineation of Clinical Target Volumes for Cervical Cancer Radiotherapy on CT/MR Dual-Modality Images. J Digit Imaging. Inform. med. (2024). https://doi.org/10.1007/s10278-023-00951-5
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