- UCLA Bioengineering
Shiwen Shen is the fourth year Ph.D Candidate in Medical Imaging Informatics (MII) Group of University of Carlifornia, Los Angeles (UCLA). He is also the Graduate Student Reseacher in Radiological Science of UCLA. He was a Data Scientist Intern in Uber in the summer of 2016. Before joining MII, he worked as a research intern in the healthcare department of Philips Research Asia. He obtained his Master's degree in Electrical Engineering from Shanghai Jiao Tong University in 2012. His research interests include medical image analysis, longitidual data analysis, Bayesian Modeling, statistical modeling and deep learning. He is also insterested in software development. In UCLA, Shiwen has worked as a main contributor in National Science Foundation (NSF) funded project Center for Domain Specific Computing (CDSC), developing computer-aided diagnosis system for lung nodulde detection in CT images. He has also led a team working on estimating multi-state disease progression utilizing Bayesian model. He is now working on developing novel hybrid lung cancer diagnosis models using deep learning and transfer learning techniques. Shiwen has published several peer-reviewed manuscripts in Computers in Biology and Medicine, Energy Minimization Methods in Computer Vision and Pattern Recognition and several IEEE conferences. He is a student member of American Medical Informatics Association and he also serves as reviewer for high impacted journals, such as Artificial Intelligence in Medicine.
- Shen S, Han SX, Petousis P, Weiss RE, Meng F, Bui AAT, Hsu W. A Bayesian model for estimating multi-state disease progression. Comput Biol Med. 2017;81:1111–120. DOI: 10.1016/j.compbiomed.2016.12.011. PDF
- Shen S, Zhong X, Hsu W, Bui AAT, Wu H, Kuo M, Raman S, Margolis DJA, Sung KH. Quantitative MRI-Driven Deep Learning for Detection of Clinical Significant Prostate Cancer. 24th Intl Soc Magnetic Resonance in Medicine (ISMRM) Annual Meeting. Singapore; 2016.
- Shen S, Bui AAT, Cong J, Hsu W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput Biol Med. 2015;57:139–49. DOI: 10.1016/j.compbiomed.2014.12.008. PDF
- Duggan N, Bae E, Shen S, Hsu W, Bui AAT, Jones E, Glavin M, Vese L. A technique for lung nodule candidate detection in CT using global minimization methods. Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer International Publishing; 2015. PDF
- Shen S, Han SX, Petousis P, Meng F, Bui AAT, Hsu W. Continuous Markov Model Approach Using Individual Patient Data to Estimate Mean Sojourn Time of Lung Cancer. AMIA Annu Symp Proc. San Francisco, CA, USA; 2015.