Deep medical image reconstruction and analysis

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With deep learning being a paradigm shifter, a lot of innovations have also been made in medicine, ranging from medical image understanding to health record analysis. In this talk, recent advancement in medical image reconstruction and analysis carried by our group at RPI will be presented. The involved tasks include low-dose CT image denoising, compressive macroscopic fluorescence lifetime image reconstruction, medical imaging registration, and image-based cancer detection. Motivated by different clinical applications, we have developed several deep learning techniques for reconstructing medical images with higher quality and much faster speed and learning new similarity metrics for improved multi-modality image registration. The proposed methods and achieved experimental results will be given in the talk.



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  • Date: 07 Mar 2018
  • Time: 10:30 AM to 11:30 AM
  • All times are MST
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  • Boise State University
  • Boise, Idaho
  • United States 83725
  • Building: MEC
  • Room Number: 114


  Speakers

Pingkun Yan

Topic:

Deep medical image reconstruction and analysis

With deep learning being a paradigm shifter, a lot of innovations have also been made in medicine, ranging from medical image understanding to health record analysis. In this talk, recent advancement in medical image reconstruction and analysis carried by our group at RPI will be presented. The involved tasks include low-dose CT image denoising, compressive macroscopic fluorescence lifetime image reconstruction, medical imaging registration, and image-based cancer detection. Motivated by different clinical applications, we have developed several deep learning techniques for reconstructing medical images with higher quality and much faster speed and learning new similarity metrics for improved multi-modality image registration. The proposed methods and achieved experimental results will be given in the talk.

Dr. Pingkun Yan is an Assistant Professor at the Department of Biomedical Engineering at Rensselaer Polytechnic Institute (RPI). Before joining RPI, he was a Senior Scientist of Philips Research working at the clinical site at the National Institutes of Health (NIH). His research interests are in translational medicine focusing on medical imaging informatics and interventional oncology guidance using machine learning and computer vision techniques.