Multimodality Imaging, Volume 1 : Deep learning applications

Jasjit S Suri

ISBN 10: 0750322454 / ISBN 13: 9780750322454

Published by IOP Publishing Ltd Dez 2022, 2022

About this Item

Neuware - This book provides technical details of the application of deep learning and machine learning in medical imaging for diagnosis brain, cardiovascular, liver, lung diseases. The text also explores the technical and ethical aspects of deep learning and machine learning applications in medical imaging.

Seller Inventory # 9780750322454

Bibliographic Details

• Title: Multimodality Imaging, Volume 1 : Deep learning applications
• Publisher: IOP Publishing Ltd Dez 2022
• Publication Date: 2022
• Language: English
• ISBN 10: 0750322454
• ISBN 13: 9780750322454
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 19D millimeters 254 millimeters width by 178 millimeters height by 19 millimeters depth
• Weight: 617 grams

About this title

Synopsis

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