Deep Learning for COVID Image Analysis
Will be published approx. on 1. January 2029
Book
Paperback/Softback
350 pages
978-0-323-90107-9 (ISBN)
Description
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 that support handling of the disease. 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 the detection of Coronavirus positive patients as well as quantifying the disease burden.
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 the detection of Coronavirus positive patients as well as quantifying the disease burden.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Illustrations
Approx. 150 illustrations (50 in full color)
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-0-323-90107-9 (9780323901079)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Hayit Greenspan, PhD is focused on developing deep learning tools for medical image analysis, as well as their translation to the clinic. She is a Professor of Biomedical Engineering with the Faculty of Engineering at Tel-Aviv University (on Leave), and currently with the Department of Radiology and the AI and Human Health Department at the Icahn School of Medicine at Mount Sinai, NYC. She is the Director of the AI Core at the Biomedical Engineering and Imaging (BMEII) Institute and the Co-director of a new AI and emerging technologies PhD program at Mount Sinai. Dr. Greenspan is also a co-founder of RADLogics Inc., a startup company bringing AI tools to clinician support S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).
Content
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
2. Segmentation and Severity analysis
3. Predictive Analysis
4. Infrastructures needed on a national and international level
5. Adaptation from research to Clinic