Deep Learning for COVID Image Analysis (häftad)
Häftad (Paperback)
Antal sidor
Academic Press
Zhou, S. Kevin
Approx. 150 illustrations (50 in full color); Illustrations, unspecified
Antal komponenter
Deep Learning for COVID Image Analysis (häftad)

Deep Learning for COVID Image Analysis

Häftad Engelska, 2021-10-01
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Medical imaging is playing a role in the fight against COVID-19, in some countries as a key tool, from the screening and diagnosis through the entire treatment procedure. The extraordinarily rapid spread of this pandemic has demonstrated that a new disease entity with a subset of relatively unique characteristics can pose a major new clinical challenge that requires new diagnostic tools in imaging. The AI/Deep Learning Imaging community has shown in many recent publications that rapidly developed AI-based automated CT and Xray image analysis tools can achieve high accuracy in detection of Coronavirus positive patients as well as quantifying the disease burden. The typical developmental cycle and large number of studies required to develop AI algorithms for various disease entities is much too long to respond effectively to produce these software tools on demand. This suggests the strong need to develop software more rapidly, perhaps using transfer learning from existing algorithms, to train on a relatively limited number of cases, and to train on multiple datasets in various locations that may not be able to be easily combined due to privacy and security issues.

Deep Learning for COVID Image Analysis provides a comprehensive overview of the most recently developed deep learning-based systems and solutions for COVID-19 image analysis, assembling a collection of state-of-the-art works for detection, severity analysis and predictive analysis, all of which are tools to support handling of the disease.

  • Provides a comprehensive overview of research work on deep learning for COVID-19 image analysis
  • Offers proven deep learning algorithms for medical image analysis applications
  • Presents the research challenges in approaching a new disease
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Övrig information

Hayit Greenspan is a Tenured Professor at the Biomedical Engineering Dept. Faculty of Engineering, Tel-Aviv University. She was a visiting Professor at the Radiology Dept. Stanford University, and is currently affiliated with the International Computer Science Institute (ICSI) at Berkeley. Dr. Greenspan's research focuses on image modeling and analysis, deep learning, and content-based image retrieval. Research projects include: Brain MRI research (structural and DTI), CT and X-ray image analysis - automated detection to segmentation and characterization. Dr. Greenspan has over 150 publications in leading international journals and conference proceedings. She has received several awards and is a coauthor on several patents. Currently her Lab is funded for Deep Learning in Medical Imaging by the INTEL Collaborative Research Institute for Computational Intelligence (ICRI-CI). Dr. Greenspan is a member of several journal and conference program committees, including SPIE medical imaging, IEEE_ISBI and MICCAI. She is an Associate Editor for the IEEE Trans on Medical Imaging (TMI) journal. Recently she was the Lead guest editor for an IEEE-TMI special Issue on "Deep Learning in Medical Imaging, May 2016. Professor S. Kevin Zhou obtained his PhD degree from University of Maryland, College Park. He is a Professor at Chinese Academy of Sciences. Prior to this, he was a Principal Expert and a Senior R&D director at Siemens Healthcare. Dr. Zhou has published 180+ book chapters and peer-reviewed journal and conference papers, registered 250+ patents and inventions, written two research monographs, and edited three books. His two most recent books are entitled "Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches, SK Zhou (Ed.)" and "Deep Learning for Medical Image Analysis, SK Zhou, H Greenspan, DG Shen (Eds.)." He has won multiple awards including R&D 100 Award (Oscar of Invention), Siemens Inventor of the Year, and UMD ECE Distinguished Aluminum Award. He has been an associate editor for IEEE Transactions on Medical Imaging and Medical Image Analysis, an area chair for CVPR and MICCAI, a board member of the MICCAI Society. Professor Zhou is a Fellow of AIMBE.


1. Detection (CT, Xray, US)
2. Segmentation and Severity analysis
3. Predictive Analysis
4. Infrastructures needed on a national and international level
5. Adaptation from research to Clinic