Multimodality Imaging, Volume 1
Description
Deep Learning has had a major impact on the healthcare industry. With the availability of enhanced computational power and the availability of graphical processing units, deep learning in healthcare has performed better than machine learning paradigms. These improvements are further propelled by the open source nature of deep learning and lower computer hardware prices. There are great potentials in healthcare due to the increase in fully automated processes that are designed to be robust.
This reference text answers several unanswered questions in both the technical and ethical aspects of deep learning and machine learning applications in medical imaging. The book provides an introduction to deep learning and machine learning. It explores the way in which these tools can be successfully applied to different areas of diagnosis of patients through segmentation and classification medical images. The text presents some of the foundational works of application of deep learning in medical diagnosis and explores the ethicality and legal aspects of AI in medical diagnosis, such as wrong diagnosis leading to fatality. Different imaging modalities of the brain, carotid artery, heart and vascular and Covid-19 are also covered.
The text provides medical professionals with insights in identifying problems early on, offering patient treatment that is both tailored and relevant.
Key Features:
- Discusses various diseases related to lung, heart, peripheral arterial imaging, as well as gene expression characterization and classification
- Explores imaging applications, their complexities and the Deep Learning models employed to resolve them in detail
- Provides state-of-the-art contributions while addressing doubts in multimodal research
- Details the future of deep learning and big data in medical imaging
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Persons
Professor Mainak Biswas is a computer scientist with specialization in the application of machine learning and deep learning in biomedical domain. His research is inspired from providing an effective solution for computer aided diagnosis for diverse diseases. His PhD specialization was in application of advanced machine learning and deep learning in complex tissue characterization and segmentation from ultrasound images of liver and carotid arteries. Dr. Biswas obtained his PhD from National Institute of Technology Goa.
Professor Jasjit S. Suri has spent over 30 years in the field of biomedical engineering/sciences, software and hardware engineering and its management. He received his Masters from University of Illinois, Chicago and Doctorate from University of Washington, Seattle. Dr. Suri was crowned with President's gold medal in 1980, one of the youngest Fellow of American Institute of Medical and Biological Engineering (AIMBE) for his outstanding contributions at Washington DC in 2004 and was also a recipient of Marquis Life Time Achievement Award for his outstanding contributions in 2018.
Content
1 Deep Learning and Augmented Radiology
2 Deep Learning in Biomedical Imaging
Deep Learning in Brain imaging
3 A Review on Artificial Intelligence in Brain Tumor Classification and Segmentation
4 MRI-based Brain Tumor Classification and its Validation: A Transfer Learning Paradigm
5 Magnetic Resonance-based Wilson Disease Tissue Characterization in Artificial Intelligence Framework using Transfer Learning
Deep Learning in Cardiovascular imaging
6 Artificial Intelligence based Carotid Plaque Tissue Characterization and Classification from Ultrasound images using a Deep Learning Paradigm
7 Quantification of plaque volume using Dual-stage deep learning paradigm
8 Stenosis measurement from ultrasound carotid artery images in the deep learning paradigm
9 A review on conventional measurement of plaque burden and deep learning models for measurement of plaque burden
Machine and Deep Learning in Liver imaging
10 Ultrasound Fatty Liver Disease Risk Stratification Using an Extreme Learning Machine Framework
11 Symtosis: Deep Learning-based Liver Ultrasound Tissue Characterization and Risk Stratification
Deep Learning in COVID19
12 Characterization of COVID19 severity in infected Lung via Artificial Intelligence-Transfer Learning
