Li Chen

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Medical Image Analysis, Machine Learning and Computer Vision


Sep. 2016 – Aug. 2021

Ph.D. in Electrical and Computer Engineering, University of Washington

Research assistant in Vascular Imaging Lab. Co-advised by Prof. Chun Yuan and Prof. Jenq-Neng Hwang
Overall   GPA:  3.89 /4.0

Sep. 2012 – June 2016

B.S. in Electrical Engineering, Fudan University

Overall   GPA:  3.6 /4.0      Ranking:  9%

Jan. 2015 – May. 2015

Exchange Student in National University of Singapore

Overall   GPA:  5.0 /5.0


Oct. 2021 – Now

Research Scientist, Philips Research North America

Ultrasound AI Group, Department of Precision Diagnosis and Image-Guided Therapy, Philips Research Innovation Hub Cambridge MA. Managed by Dr. Alvin Chen.

May – Aug. 2021

Research Intern, Genentech, Inc.

Early Clinical Development Informatics (ECDi) group. Advised by Dr. Reza Negahdar.

June – Sep. 2020

Research Intern, United Imaging Intelligence America

Medical AI group. Advised by Dr. Shanhui Sun and Terrence Chen.


Philips Research North America

Oct. 2021 ~ Now

Research Scientist. Ultrasound AI group.

Cambridge, MA

  • AI Based Medical Ultrasound Imaging Analysis

    • Develop detection and classification models for AI based lung ultrasound diagnosis.

Genentech, Inc.

May ~ Aug. 2021

Research intern. Advised by Dr. Reza Negahdar, Early Clinical Development Informatics (ECDi) group.

South San Francisco, CA

  • Imaging and non-imaging feature fusion

    • Developed a feature fusion model for improving COVID-19 classification from CTA images.
    • Fusion of imaging and non-imaging features improves COVID classification by 6.6%.
    • Grad-CAM to interpret image region importance.

United Imaging Intelligence America

June ~ Sep. 2020

Research Intern. Co-adviced by Dr. Shanhui Sun and Terrence Chen, Medical AI group

Boston, MA

  • Landmark tracking for stent enhancement

    • Developed a marker detector with 95% recall / 50% precision / 44 frames per second.
    • Robust marker tracking using graph search with 0.36mm mean distance with manual labels.

Vascular Imaging Lab & Information Processing Lab, University of Washington

Sep. 2016 ~ Aug. 2021

Research Assistant. Co-adviced by Prof. Chun Yuan and Prof. Jenq-Neng Hwang

Seattle, WA

  • Quantitative intracranial artery modeling and vascular feature extraction

    • Developed iCafe (C++ software, 60k C++ lines) for intracranial artery tracing and labeling.
    • A novel artery refinement algorithm through optimization on Curved Planar Reformation view.
    • A novel artery naming algorithm using Graph Neural Network and hierarchical refinement.
    • Created a database of 1000+ scans of cerebral vasculature models.
    • iCafe used by 12+ sites on dozens of medical research studies (aging, dementia, artery revascularization, etc.).
    • Three first-author journal papers (1,2,3) and seven conference publications ranging from technical development, validation and medical applications.
    • Editor's pick by Magnetic Resonance in Medicine.
  • Fully automated vessel wall segmentation and quantification

    • 3D region of interest identification using object tracking (Yolo V2 detector and tracklet refinement).
    • A novel vessel wall segmentation algorithm in polar coordinate system using deep learning (paper)
    • Transfer learning and active learning techniques for adaptation to multiple vascular beds.
    • Validated on 48k popliteal arteries (paper). Workload of 70 years for expert human reader.
    • Winner for American Heart Association/Amazon Web Services Prize Competition.
  • Carotid atherosclerotic lesion screening for high risk population

    • A 5-minute MR screening workflow using deep learning for cardiovascular risk assessments.
    • Modules include fast imaging, image quality assessment, artery detection, and lesion classification.
    • Assist radiologists by warning potential locations of advance lesions visualized in 3D view.
    • High agreements with expert radiologist (0.9+ sensitivity/specificity).
    • Effective in reducing manual review time for both experts and novice radiologists in clinical reading.

Medical Imaging Labs, Fudan University

Oct. 2013 ~ June 2016

Undergraduate Researcher, Project leader, Adviced by Dr. Huiliang Shang and Dr. Yi Guo

Shanghai, China

  • Modernizing Traditional Chinese Medicine diagnosis methods

    • Created or improved algorithms in tongue image analysis and data-mining of prescriptions.
    • 4 related publications (1,2,3,4) as leading authors.
  • Capstone research: Vascular image registration by circuit simulation

    • A novel algorithm to represent vasculatures as circuits for robust matching. (paper)
    • Best undergraduate thesis in Fudan University.

For Whole Research Experience, Link to Project Journey


  1. Li Chen, Wenjin Liu, Niranjan Balu, Deep Open Snake Tracker for Vessel Tracing. MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, 2021, Strasbourg, France (September 27th to October 1st). DOI: 10.1007/978-3-030-87231-1_56

  2. Li Chen, Huilin Zhao, Hongjian Jiang, Domain Adaptive and Fully Automated Carotid Artery Atherosclerotic Lesion Detection using an Artificial Intelligence Approach (LATTE) on 3D MRI. Magnetic resonance in medicine (IF:3.6), 2021, 86 (3), Pages 1662-1673. DOI: 10.1002/MRM.28794

  3. Li Chen, Thomas Hatsukami, Jenq-Neng Hwang, Automated Intracranial Artery Labeling using a Graph Neural Network and Hierarchical Refinement. MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention, 2020, Lima, Peru (October 4 - 8, 2020). DOI: 10.1007/978-3-030-59725-2_8 (pdf) (ppt) (code)

  4. Li Chen, Jie Sun, Gador Canton, Automated Artery Localization and Vessel Wall Segmentation of Vessel Wall Images using Tracklet Refinement and Polar Conversion . IEEE Access, 2020, 8, Pages 217603-217614. DOI: 10.1109/ACCESS.2020.3040616 (pdf) (code)

  5. Li Chen, Gador Canton, Wenjin Liu, Fully automated and Robust Analysis Technique for Popliteal Artery Vessel Wall Evaluation (FRAPPE) using Neural Network Models from Standardized Knee MRI. Magnetic resonance in medicine (IF:3.6), 2020, 84, Pages 2147–2160. DOI: 10.1002/mrm.28237 (pdf)

  6. Li Chen, Gaoang Wang, Niranjan Balu, Simultaneous Intracranial Artery Tracing and Segmentation from Magnetic Resonance Angiography by Joint Optimization from Multiplanar Reformation. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting, First International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI, 2019, Shenzhen, China (October 13), Pages 201-209. DOI: 10.1007/978-3-030-33327-0 (pdf) (ppt)

  7. Li Chen, Jie Sun, Daniel S Hippe, Quantitative Assessment of the Intracranial Vasculature in an Older Adult Population using iCafe (intraCranial Artery Feature Extraction). Neurobiology of Aging (IF:4.4), 2019, 79 (July 2019), Pages 59-65. DOI: 10.1016/j.neurobiolaging.2019.02.027 (pdf)

  8. Li Chen, Mahmud Mossa‐Basha, Niranjan Balu, Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open‐curve active contour vessel tracing. Magnetic resonance in medicine (IF:3.9), 2018, 79 (6), Pages 3229-3238. DOI: 10.1002/mrm.26961 (Editor's pick) (pdf) (ppt)

  9. Li Chen, Yanjun Xie, Jie Sun, 3D intracranial artery segmentation using a convolutional autoencoder. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2017, Kansas City, MO, USA (November 13 - 16). DOI: 10.1109/BIBM.2017.8217741 (pdf) (ppt)

  10. Li Chen, Yuxi Lian, Yi Guo, A vascular image registration method based on network structure and circuit simulation. BMC bioinformatics (IF:2.5), 2017, 18 (1), Pages 229. DOI: 10.1186/s12859-017-1649-1 (pdf)

For Whole Publications, Link to Curriculum Vitae

Technical Skills


2019, 2020

Magna Cum Laude Merit Award, ISMRM


Outstanding Research Award, OCSMRM


Mathematical Contest in Modeling 2015 (Meritorious Winner, top 10%)


Six consecutive years of Shu Ping Scholarship(top 3%)


Two consecutive years of Fudan university scholarship(top 15%)


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