I am currently an AI Scientist at Philips Cambridge. Previously, I was a postdoc at the Center for Advanced Medical Computing and Analysis, Massachusetts General Hospital and Harvard Medical School. I received my Ph.D. in Electrical and Computer Engineering at the Johns Hopkins University. Before that, I obtained my B.S. in Radiological Engieering and a minor in Physics from the University of Illinois at Urbana-Champaign.
The ultimate objective in my career is to remain an engaged researcher in the realm of healthcare AI, acting as a conduit for the translation of research findings in Computer Vision to clinical applications. My current role at Philips is focused on developing generative AI frameworks to create synthetic Ultrasound images that replicate real-world pathological patterns. Previously, I worked on developing state-of-the-art AI algorithms for detecting and classifying physiological and pathological patterns in medical Ultrasound data. During my Ph.D., I designed and studied AI models that can be used as surrogate for radiologist to perform clinical defect detection tasks in renal SPECT.
Prior to graduate school, I worked on developing virtual reality (VR) courses with Prof. Rizwan Uddin. These VR courses are now part of our department's curriculum and had been used by incoming freshmen as supplements to the real experiments since their first launch in 2012. Here is a video record of one of the courses we developed.
In my free time, I enjoy playing Basketball. Here is a video record of one of our highlight games.
Here is my CV and Google Scholar profile . I can be reached by email at ye.li@philips.com.
Weakly Semi-Supervised Detector-Based Video Classification with Temporal Context for Lung Ultrasound
G.Y. Li, L. Chen, M. Zahiri, N. Balaraju, S. Patil, C. Mehanian, C. Gregory, K. Gregory, B. Raju, J. Kruecker, and A. Chen
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023
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Ultrasound Image Synthesis Using Generative AI for Lung Consolidation Detection
Y. Chou, G.Y. Li, L. Chen, M. Zahiri, N. Balaraju, S. Patil, B. Hicks, N. Schnittke, D. Kessler, J. Shupp, M.
Parker, C. Baloescu, C. Moore, C. Gregory, K. Gregory, B. Raju, J. Kruecker, and A. Chen
Under Review, 2024
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Spatiotemporal Learning with Context-aware Video Tubelets for
Ultrasound Video Analysis
G.Y. Li, L. Chen, B. Hicks, N. Schnittke, D. Kessler, J. Shupp, M. Parker, C. Baloescu, C. Moore, C. Gregory,
K. Gregory, B. Raju, J. Kruecker, and A. Chen
Under Review, 2024
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Weakly Semi-Supervised Detection in Lung Ultrasound Videos
J. Ouyang, L. Chen, G.Y. Li, N. Balaraju, S. Patil, C. Mehanian, S. Kulhare, R. Millin, .K.W. Gregory, C.R. Gregory, M. Zhu, D.O. Kessler, L. Malia, A. Dessie, J. Rabiner, D. Coneybeare, B. Shopsin, A. Hersh, C. Madar, J. Shupp, L.S. Johnson, J. Avila, K. Dwyer, P. Weimersheimer, B. Raju, J. Kruecker, and A. Chen
International conference on Information Processing in Medical Imaging (IPMI), 2023
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Girth-based Administered Activity for Pediatric 99mTc-DMSA SPECT
Y. Li, J.L. Brown, J. Xu, J. Chen, M. Ghaly, M. Dugan, X. Cao, Y. Du, F.H. Fahey, W. Bolch, G. Sgouros, and E.C. Frey
Medical Physics, 2023
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SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images
G.Y. Li, J. Chen, S. Jang, K. Gong, Q. Li
Medical Physics, 2023
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TransMorph: Transformer for unsupervised medical image registration
J. Chen, E.C. Frey, Y. He, W.P. Segars, Y. Li, Yong Du
Medical Image Analysis, 2022
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ViT-V-Net: Vision Transformer for Unsupervised Volumetric Medical Image Registration
J. Chen, Y. He, E.C. Frey, Y. Li, Yong Du
Medical Imaging with Deep Learning, 2022
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A Noise-Level-Aware Framework for PET Image Denoising
Y. Li, J. Cui, J. Chen, G. Zeng, S. Wollenweber, F. Jansen, S. Jang, K. Kim, K. Gong, Q. Li.
MLMIR: International Workshop on Machine Learning for Medical Image Reconstruction, 2022
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DeepAMO: A Multi-slice, Multi-view Anthropomorphic Model Observer for Visual Detection Tasks Performed on Volume Images
Y. Li, J. Chen, J. Brown, S.T. Treves, X. Cao, F.H. Fahey, G. Sgouros, W.E. Bolch, and E.C. Frey.
Journal of Medical Imaging’s special section: Perspectives in Human and Model Observer Performance, 2021
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Generating Anthropomorphic Phantoms Using Fully Unsupervised Deformable Image Registration with Convolutional Neural Networks
J. Chen, Y. Li, Y. Du, and E.C. Frey
Medical Physics (Editor's choice), 2020
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Learning Fuzzy Clustering for SPECT Segmentation via Convolutional Neural Networks
J. Chen, Y. Li, S.P. Rowe, H.W. Chung, Y. Du, L.B. Solnes, M.A. Jacobs, and E.C. Frey
Medical Physics, 2020
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Current Pediatric Administered Activity Guidelines for 99mTc-DMSA SPECT Based on Patient Weight Do Not Provide the Same Task-based Image Quality
Y. Li, S. O’Reilly, D. Plyku, S.T. Treves, F.H. Fahey, Y. Du, X. Cao, J. Brown, G. Sgouros, W.E. Bolch, and E.C. Frey.
Medical Physics, 2019
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A Projection Image Database to Investigate Factors Affecting Image Quality in Weight-based Dosing: Application to Pediatric Renal SPECT
Y. Li, S. O’Reilly, D. Plyku, S.T. Treves, Y. Du, F.H. Fahey, X. Cao, A.K. Jha, G. Sgouros, W.E. Bolch, and E.C.Frey
Physics in Medicine and Biology, 2018
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Teaching notes and presentations:
List of notes on a few key topics in probabilistic inference and machine learning:
Graduate Board Oral Exam notes:
List of lecture notes: