|
|
|||
Homepage of Dicheng Chen(English, 中文) |
||||
| Ph.D. candidate | ||||
| Department of Electronic Science,School of Electronic Science and Engineering (National Model Microelectronics College), Xiamen University. | ||||
| Email:dcchen@stu.xmu.edu.cn | ||||
| Computational Sensing Group at Xiamen University | ||||
| |||||||||
Biosketch |
|||||||||
Dicheng Chen is a PhD candidate of Department of Electronic Science at Xiamen University in China.
|
|||||||||
 |
Deep learning |
|
Medical signal processing | ||||||
|
Magnetic resonance spectroscopic imaging |
||||||||
|
Ph.D. candidate (Sept. 2019-Current) Advisor: Xiaobo Qu Major : Electronics and Communication Engineering. Xiamen University, Fujian Province, China. |
||||||||
|
Graduate Student (Sept. 2016-June 2019) Advisor: Prof. Dazhi Jiang Major : Department of Computer Science, ShanTou University, Guangdong Province, China. |
||||||||
|
B.S. (Sept. 2011-June 2015) Department of Computer Science, |
||||||||
| Research and Teaching Experiences | |||||||||
| (a) Medical biological signal processing and analysis | |||||||||
|
Research on automatic recognition of emotional state for non-contact, unstable and long time scale physiological signals. |
||||||||
|
Research on multimodal physiological signals for VDT operators. |
||||||||
|
Research on health diagnosis based on massive sign signal data. |
||||||||
| (b) Deep learning | |||||||||
|
A three-layer auto-coded neural network combining time window, empirical modal decomposition and deep learning was studied to predict the occurrence of acute hypotension. |
||||||||
|
In order to improve the interpretability of medical models, we studied hybrid artificial intelligence models of deep learning, multi-gene expression programming (GEP) and fuzzy expert system. |
||||||||
|
To study diagnosis and prognosis of cancer based on cell distribution characteristics. |
||||||||
| Professional Skills | |||||||||
| (a) Medical biological signal processing | |||||||||
|
Biological blood pressure signal processing. | ||||||||
|
Magnetic resonance Spectral processing. | ||||||||
| (b) Deep learning | |||||||||
|
Deep learning framework: tensorflow, torch. |
||||||||
|
Mastered programming languages: Python, Matlab. |
||||||||
| (a) Journal paper | |||||||||
|
D. Chen et al. Magnetic resonance spectroscopy quantification aided by deep estimations of imperfection factors and overall macromolecular signal, IEEE Trans. Biomed. Eng., DOI: 10.1109/TBME.2024.3354123, 2024. (SCI, JCR 2, IF 4.60) |
||||||||
|
D. Chen et al. Magnetic resonance spectroscopy deep learning denoising using few in vivo data, IEEE Trans. Comput. Imaging, vol. 9, pp. 448-458, 2023. (SCI, JCR 2, IF 5.40) |
||||||||
|
D. Chen et al. Review and prospect: Deep learning in nuclear magnetic resonance spectroscopy, Chem. Eur. J., vol. 26, no. 46, pp. 10391-10401, 2020. (SCI, JCR 2, TOP Journal, IF: 5.20) |
||||||||