Multimodality Imaging, Volume 1

Deep learning applications

By (author) Jasjit Suri, Professor Mainak Biswas

Publication date:

20 December 2022

Length of book:

356 pages

Publisher

Institute Of Physics Publishing

Dimensions:

254x178mm
7x10"

ISBN-13: 9780750322423

BOOK DESCRIPTION
TABLE OF CONTENTS

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

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