Deep Learning in Medical Imaging: A Comprehensive Guide

Authored by renowned expert Jasjit S Suri, this cutting-edge research and reference text delves into the intricacies of Deep Learning models, providing an in-depth analysis of popular models such as convolution neural networks (CNN), deep belief networks (DBN), autoencoders, and residual neural networks (Res Nets).

Applications of Deep Learning in Medical Imaging

The text explores various applications of Deep Learning in medical imaging, including gene identification, radiology, cardiovascular imaging, and fatty liver disease characterization, as well as its role in combating COVID-19. These applications are crucial for medical professionals and researchers in the field of AI in medical imaging.

Key Features of the Book

• Delves into diseases related to lung, heart, and peripheral arterial imaging, as well as gene expression characterization and classification
• Examines imaging applications, their complexities, and the Deep Learning models employed to resolve them in detail
• Presents state-of-the-art contributions while addressing doubts in multimodal research
• Details the future of deep learning and big data in medical imaging

Publisher: Institute of Physics Publishing; Publication Date: December 20, 2022; Language: English; ISBN: 9780750322423, 9780750322447

This comprehensive guide is a valuable resource for medical professionals, researchers, and students seeking to understand the applications of Deep Learning in medical imaging. With its in-depth analysis and real-world examples, it is an essential tool for anyone looking to stay at the forefront of this rapidly evolving field.