Perspective on Deep Imaging

Ge Wang PhD

  • Director, Biomedical Imaging Center
  • Rensselaer Polytechnic Institute

Mar 05, 2020|12:00 PM – 1:00 PM

130 University Health Services Building (Pizza & drinks served at 11:50) Please RSVP to

Artificial intelligence is now recognized as an on-going paradigm shift, with an emphasis on machine learning especially deep learning. Computer vision and image analysis are two major applications of deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic imaging produces images of multi-dimensional structures from experimentally measured data (line integrals, harmonic components, and so on, of underlying images) which are tomographic features (features to images). Since 2016, deep learning is being actively developed worldwide for tomographic imaging, forming a new area of imaging research. In this presentation, we present a perspective on deep imaging involving data processing, image reconstruction, radiomics, and beyond. We show deeptomographic results and also explore network innovations.

Slides from presentations:

Perspective on Deep Imaging

Medical Imaging in the Deep Learning Framework


Ge Wang is Clark & Crossan Endowed Chair Professor and Director of Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY, USA. He published the first spiral/helical cone-beam/multislice CT algorithm in 1991 and has since then systematically contributed over 100 papers to theory, algorithms, and biomedical applications in this important area of CT research. 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 developed interior tomography theory and algorithms to solve the long-standing “interior problem” for high-fidelity local reconstruction, enabling omnitomography (“all-in-one”) with CTMRI as an example. 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 of IEEE Access, and Associate Editor of IEEE Trans. Medical Imaging (TMI) (recognized as “Outstanding Associate Editor” by TMI), Medical Physics, and Machine Learning Science and Technology. He is Fellow of IEEE, SPIE, OSA, AIMBE, AAPM, AAAS, and NAI.