Ge Wang
Initiator for First Deep Reconstruciton Workshop @The National Institutes of Health
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WORK HISTORY
Initiator for First Deep Reconstruciton Workshop @The National Institutes of Health
Troy, NY, US
First Meeting on Deep Learning Based Tomographic Reconstruction(For more details, please visit Ge Wang, PhD, Hongming Shan, PhDComputer vision and image analysis are both great examples of successes with machine learning especially deep learning. While computer vision and image analysis primarily deal with existing images, tomographic reconstruction produces images of internal structures from externally measured indirect data. Recently, deep learning techniques are being actively explored for tomographic reconstruction by multiple groups worldwide, with encouraging results and potential major impacts. We believe that deep reconstruction is a next major target of deep learning. Sponsored by RPI Center for Biotechnology & Interdisciplinary Studies/Biomedical Imaging Center/NIH Training Program in Biomolecular Science and Engineering, we are honored to host this workshop for brainstorming and collaboration.
EDUCATION
University of Chinese Academy of Sciences
Master of Science - MS, Remote Sensing
University at Buffalo
Doctor of Philosophy - PhD, Electrical and Electronics Engineering
Xidian University
Bachelor of Engineering - BE, Signal processing
ABOUT GE WANG
Ge Wang (Fellow, SPIE, AAPM, OSA, AIMBE, and NAI) is the Clark & Crossan Endowed Chair Professor and Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute (Troy, New York, USA). He pioneered the cone-beam spiral CT method in 1991 and published the first perspective on AI-based tomographic imaging in 2016. His other notable contributions, in collaboration with his peers, include interior tomography, bioluminescence tomography, and innovative photon-counting CT algorithms. Dr. Wang\'s interests encompass AI-based imaging, teaching, and publishing. His recent honors include the 2021 EMBS Career Achievement Award, 2022 SPIE Meinel Technology Award, 2022 Sigma Xi Chubb Award for Innovation, 2023 RPI Wiley Distinguished Faculty Award, 2023 R1 Outstanding Teaching Award, 2023 NPSS/NMISC Hoffman Medical Imaging Scientist Award, 2024 TRPMS Best Paper Award, and 2025 AAPM Edith H. Quimby Award for Lifetime Achievement. As the Editor-in-Chief, he is committed to upholding TMI\'s tradition of excellence and advancing the AI4TMI initiative in collaboration with the global medical imaging community.
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