Ge Wang’s Biography: Ge Wang is Clark & Crossan Endowed Chair Professor and Director of Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, New York, USA. He published the first spiral/helical cone-beam/multi-slice CT algorithm in 1991 and has then systematically contributed many papers on this particular topic. Currently, there are 100+ million medical CT scans yearly with a majority in the spiral/helical cone-beam/multi-slice mode. Dr. Wang’s group and collaborators developed interior tomography theory and algorithms to solve the long-standing “interior problem” for high-fidelity local reconstruction, enabling omnitomography (“all-in-one” and “all at once”) with simultaneous CT-MRI as an example. He initiated the area of bioluminescence tomography with a significant impact on biophotonics. He has published 480+ journal publications, receiving a high number of citations and academic awards. His results were featured in Nature, Science, PNAS, and news media. In 2016, he published the first perspective on deep-learning-based tomographic imaging as a new direction of machine learning, and with his coauthors the first book on deep learning based tomography and the Nature Machine Intelligence paper on the superiority of deep learning over iterative reconstruction. His team has been continuously well-funded by federal agencies and leading companies, actively translating deep learning techniques into imaging products. His interests include medical imaging and machine learning. He is the Lead Guest Editor for five IEEE Transactions on Medical Imaging Special Issues, Founding Editor-in-Chief of International Journal of Biomedical Imaging, Board Member and Senior Editor of IEEE Access, and Outstanding Associate Editor of IEEE Trans. Medical Imaging, Medical Physics, and Machine Learning Science and Technology. He is Fellow of IEEE, SPIE, OSA, AIMBE, AAPM, AAAS, and NAI.
ML/AI Highlights since 2016
• 2016 First perspective on machine learning / deep learning for tomographic imaging, as a roadmap for the new area of “deep reconstruction” / “deep imaging” and a basis for the 1st special journal issue on this theme (IEEE TMI) (https://ieeexplore.ieee.org/document/7733110)
• 2017 Top 10 most downloaded articles of Med. Phys. (Wang G, Kalra M, Orton C: Machine learning will transform radiology significantly within the next 5 years, Med. Phys. 44:2041-2044, doi:10.1002/mp.12204, 2017; https://aapm.onlinelibrary.wiley.com/doi/full/10.1002/mp.12204)
• 2018 IEEE Access featured article (As a retrospective recognition) (Wang G, Perspective on deep imaging. IEEE Access, 2016) (https://ieeeaccess.ieee.org/featured-articles/deepimaging/)
• 2018 IEEE TMI featured special issue: Wang G, Ye JC, Mueller K, Fessler JA: Image Reconstruction Is a New Frontier of Machine Learning — Editorial for the Special Issue “Machine Learning for Image Reconstruction”. IEEE TMI, June, 2018 (https://ieeexplore.ieee.org/document/8359079)
• 2018 NIH Invited AI Talks: “Large, Public, Multimodal Image Datasets” and “Tomographic Reconstruction with Machine Learning”, NIH Artificial Intelligence in Medical Imaging Workshop, NIH, Bethesda, MD, https://www.nibib.nih.gov/news-events/meetings-events/artificial-intelli..., August 23, 2018
• 2019 roadmap article in Radiology: “A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop” by Langlotz CP et al.( https://pubs.rsna.org/doi/10.1148/radiol.2019190613)
• A series of journal papers on deep neural networks for low-dose, sparse-data, super-resolution, and other CT topics
• Strategic long-term partnerships funded by GE and Hologic in the machine learning based CT and tomosynthesis areas respectively in 2018; and AI PhD Mentoring Grant funded by IBM in 2019
• NIH R01 on deep radiomics with Memorial Sloan Kettering and NIH AIP/R01 on deep hybrid imaging with MARS Inc. (rated Top 1%), funded in 2019
• Editorial Board Member for the IOP Journal “Machine Learning: Science and Technology”, https://iopscience.iop.org/journal/2632-2153, 2019
• Wang G, et al., Textbook “Machine Learning for Tomographic Imaging”, IOP, 2019 (The first of its kind in this area, highlighted in “Physics World”, March, 2019, https://physicsworld.com/a/a-machine-learning-revolution/)
• 2019 Nature Machine Intelligence Article (vol. 1, pp. 269–276, 2019): “Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction” by Shan HM, Padole A, Homayounieh F, Kruger U, Khera RD, Nitiwarangkul C, Kalra MK, Wang G (https://www.eurekalert.org/pub_releases/2019-06/rpi-mla060919.php).