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Deep Learning for COVID Image Analysis
- 1st Edition - January 1, 2029
- Editors: Hayit Greenspan, S. Kevin Zhou
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 0 1 0 7 - 9
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 0 2 0 2 - 1
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,… Read more
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Request a sales quoteDeep 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.
- 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 research challenges in approaching a new disease
University and industry researchers and practitioners in medical imaging; graduate students studying deep learning for medical imaging with applications to COVID-19. Radiologists who are familiar with AI-based tools and may want to understand the capability of automated tools to provide support in a pandemic
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
- No. of pages: 350
- Language: English
- Edition: 1
- Published: January 1, 2029
- Imprint: Academic Press
- Paperback ISBN: 9780323901079
- eBook ISBN: 9780323902021
HG
Hayit Greenspan
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
Affiliations and expertise
Head, Medical Image Processing and Analysis Lab, Biomedical Engineering Department, Faculty of Engineering, Tel-Aviv University, IsraelSZ
S. Kevin Zhou
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).
Affiliations and expertise
Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA