Mr. Thomas Z. Li Profile

Thomas Z. Li

SPIE Involvement:

Author

Thomas is an MD/Ph.D. candidate at Vanderbilt University in the Medical-image Analysis and Statistical Interpretation Lab. He studied computer science at Duke University and has held software engineering roles in industry. His research is centered around combining AI with longitudinal and multi-modal medical data to advance the management of pulmonary nodules and lung cancer.

Proceedings Article | 3 April 2024 Presentation + Paper

Nucleus subtype classification using inter-modality learning

Lucas Remedios, Shunxing Bao, Samuel Remedios, Ho Hin Lee, Leon Cai, Thomas Li, Can Cui, Jia Li, Qi Liu, Ken Lau, Joseph Roland, Mary Washington, Lori Coburn, Keith Wilson, Yuankai Huo, Bennett Landman, Ruining Deng

Proceedings Volume 12933, 129330F (2024) https://doi.org/10.1117/12.3006237

KEYWORDS: Machine learning, Colon, Pathology, Multiplexing, Human physiology, Clinical research, Biological imaging, Artificial intelligence

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Proceedings Article | 2 April 2024 Presentation + Paper

Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation

Aravind Krishnan, Kaiwen Xu, Thomas Li, Lucas Remedios, Chenyu Gao, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Fabien Maldonado, Kim Sandler, Ivana Išgum, Bennett Landman

Proceedings Volume 12926, 129261D (2024) https://doi.org/10.1117/12.3006608

KEYWORDS: Emphysema, Bone, CT reconstruction, Computed tomography, Data modeling, Lung, Image analysis, Medical image reconstruction

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Proceedings Article | 2 April 2024 Poster + Paper

Learning site-invariant features of connectomes to harmonize complex network measures

Nancy Newlin, Praitayini Kanakaraj, Thomas Li, Kimberly Pechman, Derek Archer, Angela Jefferson, Bennett Landman, Daniel Moyer

Proceedings Volume 12930, 129302E (2024) https://doi.org/10.1117/12.3009645

KEYWORDS: Education and training, Brain, Scanners, Diffusion, Alzheimer disease, Diffusion magnetic resonance imaging, Data modeling, Biological imaging, Matrices, Magnetic resonance imaging

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SPIE Journal Paper | 18 July 2023

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

Thomas Z. Li, Ho Hin Lee, Kaiwen Xu, Riqiang Gao, Benoit M. Dawant, Fabien Maldonado, Kim L. Sandler, Bennett A. Landman

JMI, Vol. 10, Issue 04, 044002, (July 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044002

KEYWORDS: Emphysema, Lung, Lung cancer, Computed tomography, Chronic obstructive pulmonary disease, Algorithm development, Image segmentation, Education and training, 3D modeling, Data modeling

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Proceedings Article | 11 April 2023 Presentation + Paper

Stratification of lung cancer risk with thoracic imaging phenotypes

Kaiwen Xu, Mirza Khan, Thomas Li, Riqiang Gao, Sanja Antic, Yuankai Huo, Kim Sandler, Fabien Maldonado, Bennett Landman

Proceedings Volume 12464, 1246407 (2023) https://doi.org/10.1117/12.2654018

KEYWORDS: Body composition, Computed tomography

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Proceedings Article | 10 April 2023 Presentation

Characterizing browser-based medical imaging AI with serverless edge computing: towards addressing clinical data security constraints

Chenxi Dong, Thomas Li, Kaiwen Xu, Zekun Wang, Fabien Maldonado, Kim Sandler, Bennett Landman, Yuankai Huo

Proceedings Volume 12469, 1246907 (2023) https://doi.org/10.1117/12.2653626

KEYWORDS: Artificial intelligence, Medical imaging, Computer security, 3D modeling, Computed tomography, Systems modeling, Data modeling, Clouds, Visualization, Tumor growth modeling

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Proceedings Article | 3 April 2023 Presentation + Paper

Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography

Thomas Li, Kaiwen Xu, Riqiang Gao, Yucheng Tang, Thomas Lasko, Fabien Maldonado, Kim Sandler, Bennett Landman

Proceedings Volume 12464, 1246412 (2023) https://doi.org/10.1117/12.2653911

KEYWORDS: Transformers, Lung cancer, Tumors, Lung, Chest, Medical imaging, Computed tomography, Cancer

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