Multimodality Imaging, Volume 1: Deep learning applications(IOP ebooks)

This research and reference text explores the finer details of Deep Learning models. It provides a brief outline on popular models including convolution neural networks (CNN), deep belief networks (DBN), autoencoders, residual neural networks (Res Nets). The text discusses some of the Deep Learning-based applications in gene identification. Sections in the book explore the foundation and necessity of deep learning in radiology, the application of deep learning in the area of cardiovascular imaging and deep learning applications in the area of fatty liver disease characterization and COVID19, respectively. This reference text is highly relevant for medical professionals and researchers in the area of AI in medical imaging. 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

Table of Contents:
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

About the Author :
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.