Zhiwei Gong

Zhiwei Gong

Ph.D. student

I am a First-year Ph.D. student in Artificial Intelligence at Intelligent Computing for Clinical Imaging (ICCI) Lab, University of Pittsburgh, advised by Prof. Shandong Wu. I am also affiliated with University of Pittsburgh Medical Center (UPMC). I got my M.S.E. degree in Appliematics and Statistics at Johns Hopkins University in 2023, advised by Prof. Russell H. Taylor and Prof. Swaroop S. Vedula. And I got my B.Sc. degree in Mathematics and Applied Mathematics at University of Reading and Nanjing University of Information Science & Technology in 2021.

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Interests

Medical Image Analysis
Deep Learning
Computer-aided Diagnosis

Education

Ph.D. in Artificial Intelligence
University of Pittsburgh
M.S.E. in Applied Mathematics and Statistics
Johns Hopkins University
B.Sc. in Mathematics and Applied Mathematics
University of Reading
B.Sc. in Mathematics and Applied Mathematics
Nanjing University of Information Science & Technology

📚 My Research

My research interests broadly include AI in healthcare, with a focus on medical image analysis and deep learning. I’m excited about creating AI agents that can assist radiologists in interpreting medical images and predicting patient outcomes. Currently, I am working on breast cancer diagnosis using deep learning techniques.

Please reach out to collaborate! 😃

Featured Publications

Statistical Shape Models, Eustachian Tube

Statistical Shape Model of the Eustachian Tube for Understanding and Managing Eustachian Tube Dysfunction

Highlight the current limited understanding of the variations present with the eustachian tube and demonstrate the utility of statistical shape models in quantifying variations present within the eustachian tube to explain interpatient differences based on age, sex, and pathology.

May 5, 2023

Deep Learning, Eustachian Tube Dysfunction, Medical Imaging

Statistical Shape Model of the Eustachian Tube for Understanding and Managing Eustachian Tube Dysfunction

Obtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures.

Oct 2, 2022

Recent Publications

Zhiwei Gong, Ameen Amanian, Yuliang Xiao, Aseem Jain, Manish Sahu, Francis X. Creighton (2023). Statistical Shape Model of the Eustachian Tube for Understanding and Managing Eustachian Tube Dysfunction. TRIOLOGICAL SOCIETY 125th Annual Meeting at Combined Otolaryngology Spring Meetings (Oral).

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Ameen Amanian, Yuliang Xiao, Zhiwei Gong, Deepa Galaiya, Russell H. Taylor, Mathias Unberath, Manish Sahu, Francis X. Creighton (2022). Statistical Shape Model of the Eustachian Tube for Understanding and Managing Eustachian Tube Dysfunction. Conference on Machine Intelligence in Medical Imaging (Oral).

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