DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS FOR MEDICAL IMAGE COMPUTING

ISBN 10: 3319429981 / ISBN 13: 9783319429984

Published by Springer, 2017

About this Item

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Bibliographic Details

• Title: DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS FOR MEDICAL IMAGE COMPUTING
• Publisher: Springer
• Publication Date: 2017
• Language: English
• ISBN 10: 3319429981
• ISBN 13: 9783319429984
• Binding: Hardcover
• Condition: New

About this title

Synopsis

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.

About the Author

Dr. Le Lu is a Staff Scientist in the Radiology and Imaging Sciences Department of the National Institutes of Health Clinical Center, Bethesda, MD, USA.

Dr. Yefeng Zheng is a Senior Staff Scientist at Siemens Healthcare Technology Center, Princeton, NJ, USA.

Dr. Gustavo Carneiro is an Associate Professor in the School of Computer Science at The University of Adelaide, Australia.

Dr. Lin Yang is an Associate Professor in the Department of Biomedical Engineering at the University of Florida, Gainesville, FL, USA.