COVID-19 Radiological Lung Imaging
A Classic Artificial Intelligence Framework
- 1st Edition - December 6, 2099
- Editors: Luca Saba, Sushant Agarwal, Jasjit S. Suri
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 8 7 4 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 8 7 5 - 1
COVID-19 Radiological Lung Imaging: A Classic Artificial Intelligence Framework introduces modern AI technologies for early detection of COVID-19 disease to assist in saving pa… Read more
COVID-19 Radiological Lung Imaging: A Classic Artificial Intelligence Framework introduces modern AI technologies for early detection of COVID-19 disease to assist in saving patient lives and safeguarding frontline workers. With a strong focus on Deep Learning, the book examines specific detection and classification techniques in lung X-ray imaging, computed tomography lung imaging, deep learning on edge devises and bias measurements, deep learning for cloud and explainable AI for validation, and discusses the medical impact and AI implications for COVID-19 in lung pathologies. It is therefore an ideal reference for researchers and clinicians working in radiology and pulmonary medicine to learn the modern AI technologies in COVID-19 paradigms for implementation.
- Offers broad and complete coverage in AI in healthcare regarding detection, classification, explainable AI, cloud-based diagnosis, pruning, and bias technologies in radiology
- Reviews AI systems technology that can be incorporated into medical devices as well as in many diagnoses and treatment procedures
- Contributes to early detection techniques of COVID-19 disease through AI technologies
Scientists, medical practitioners, clinicians, students, Industry, library, e-resources
Section 1: X-Ray Lung Imaging using Deep Learning
1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation
Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging
9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques
Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning
12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients
Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation
15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment
Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies
17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
1. Lung Segmentation using Lung X-ray Scans: U-Series
2. Lung Classification using Lung X-ray Scans
3. Heatmap using Explainable AI on Lung X-ray Scans
4. Lesion Segmentation using Lung X-ray Scans: Hybrid U-Series
Section 2: Computed Tomography Lung Imaging using Solo and Hybrid Deep Learning
5. Deep Learning-Based Characterization of Acute Respiratory Distress Syndrome in COVID-19-Infected Lungs
6. Hybrid Deep Learning Artificial Intelligence Models for Lung Segmentation in COVID-19 Computed Tomography Scans
7. Hybrid Deep Learning Models based on COVID-19 Lung Segmentation in Computed Tomography using Inter-Variability Framework
8. Hybrid Deep Learning in a Multicenter Framework for Automated COVID-19 Lung Segmentation
Section 3: Pruning & Optimization Deep Learning Techniques for Computed Tomography COVID-19 Imaging
9. Lesion Segmentation in COVID-19 Lung using Artificial Intelligence Framework for Automated Computed Tomography Scans
10. Artificial Intelligence-Based External Validation Framework for Computed Tomography Lung Segmentation using Italian and Croatian Cohorts
11. Pruning of COVID-19 Computed Tomography based Lung Segmentation Deep Learning Models for Storage and Performance Improvement and its Validation using Class Activation Map Techniques
Section 4: Deep Learning on Edge Devices for COVID-19 & Bias Measurements in Deep Learning
12. Deep Learning for COVID-19 deployment on Low-Cost Edge Device: Raspberry Pie
13. Systematic Review of Artificial Intelligence Based Paradigm in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients
14. Five Strategies for Bias Estimation in Hybrid Deep Learning for Acute Respiratory Distress Syndrome COVID-19 Lung Infected Patients
Section 5: Deep Learning on Cloud for COVID-19 and Explainable AI for Validation
15. Deep Learning deployment on Cloud for COVID-19 Lung Segmentation
16. Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans in a Cloud Environment
Section 6: Medical Impact and AI Application for COVID-19 in Lung Pathologies
17. Lung COVID from pathology to radiological features
18. Lung COVID and pulmonary embolism
19. Classification systems in X-ray for Lung pathology COVID based
20. Classification systems in CT for Lung pathology COVID based
21. A changing landscape: integration of AI models that incorporate lung imaging data and biological, molecular for the model of risk prediction.
- Edition: 1
- Published: December 6, 2099
- Language: English
LS
Luca Saba
Luca Saba is Dean of School of Medicine at the University of Cagliari, full professor of Radiology and Chief of the Department of Radiology in the A.O.U. of Cagliari. Professor Saba’s research is focused on Multi-Detector-Row Computed Tomography, Magnetic Resonance, Ultrasound, Neuroradiology, and Diagnostic in Vascular Sciences. His works have achieved more than 700 high impact factor in notable peer-reviewed journals such as The New England Journal of Medicine, Lancet Neurology, Circulation Radiology, American Journal of Neuroradiology, Atherosclerosis, European Radiology. He has spoken over 150 times at national and international levels and won 22 scientific and extracurricular awards during his career. Dr Saba has presented more than 500 papers and posters in National and International Congress (RSNA, ESGAR, ECR, ISR, AOCR, AINR, JRS, SIRM, AINR). He has written 43 book-chapters and is Editor of 19 books in the field of Computed Tomography, Cardiovascular, Plastic Surgery, Gynecological Imaging and Neurodegenerative imaging.
Affiliations and expertise
Dean of School of Medicine, University of Cagliari; Professor of Radiology and Chief of the Department of Radiology, A.O.U. of CagliariSA
Sushant Agarwal
Sushant Agarwal, BE, is currently a software engineer with Tech-Net Inc and has experience with working on edge devices such as Raspberry Pie, NVIDIA Jetson Nano, and Intel Neural compute stick. He is passionate about deep learning and has a good command of Python, SQL, Deep Learning, and Computer Vision. Sushant Agarwal holds Udacity Nanodegrees in Deep Learning and Data Science as well as a Specialization in Artificial Intelligence for Medical Imaging and has been a top performer in various national and international competitions. He has published over 11 research papers internationally in the field of computer vision and deep learning.
Affiliations and expertise
Software Engineer – Tech-Net Inc.JS
Jasjit S. Suri
Dr. Jasjit Suri, PhD, MBA, is a renowned innovator and scientist. He received the Director General’s Gold Medal in 1980 and is a Fellow of several prestigious organizations, including the American Institute of Medical and Biological Engineering and the Institute of Electrical and Electronics Engineers. Dr. Suri has been honored with lifetime achievement awards from Marcus, NJ, USA, and Graphics Era University, India. He has published nearly 300 peer-reviewed AI articles, 100 books, and holds 100 innovations/trademarks, achieving an H-index of nearly 100 with about 43,000 citations. Dr. Suri has served as chairman of AtheroPoint, IEEE Denver section, and as an advisory board member to various healthcare industries and universities globally.
Affiliations and expertise
Chairman, AtheroPoint LLC, USA