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Guest Editors
James S. Duncan, Michael F. Insana, and Nicholas Ayache
Publication Date
Early 2020

Biomedical Imaging and Analysis In the Age of Sparsity, Big Data, and Deep Learning

Guest Editors:

  • James S. Duncan, Yale University, USA
  • Michael F. Insana, University of Illinois, Urbana-Champaign, USD
  • Nicholas Ayache, Inria, French Research Institute for Computer Science and Applied Mathematics, France

Description:

Medical imaging of the human body using a range of modalities has revolutionized medicine over the past several decades and continues to grow at a rapid pace. More than ever, previously unknown information about biology and disease is being unveiled at a range of spatiotemporal scales. While results and adoption of strategies related to the computational and quantitative analysis of the images has lagged behind image acquisition approaches, there is a recent heavy interest and explosion of activity in this area in recent years. This special issue aims to define and highlight what some of the “hot” newer ideas that are in biomedical imaging and analysis, intending to shine a light on where the field might move in the next several decades ahead, focused on emphasizing where electrical engineers have been involved and could potentially have the most impact. These areas include image acquisition physics, image/signal processing and image analysis, including pattern recognition & machine learning.

Publication Date: 2020

Submission Deadline: March 1, 2019