Deep Learning and Convolutional Neural Networks for Medical Image Computing
Precision Medicine, High Performance and Large-Scale Datasets
Editors: Le, L., Zheng, Y., Carneiro, G., Yang, L. (Eds.)
Free Preview- Addresses the challenges of applying deep learning for medical image analysis
- Presents insights from leading experts in the field
- Describes principles and best practices
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- About this book
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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 authors
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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. - Reviews
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“This book … is very suitable for students, researchers and practitioner. In addition, the book provides an important and useful reference for experienced researchers on particular aspects of deep learning based medical image analysis.” (Guang Yang, IAPR Newsletter, Vol. 41 (2), April, 2019)
- Table of contents (17 chapters)
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Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective
Pages 3-10
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Review of Deep Learning Methods in Mammography, Cardiovascular, and Microscopy Image Analysis
Pages 11-32
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Efficient False Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation
Pages 35-48
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Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning
Pages 49-61
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A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set
Pages 63-72
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Table of contents (17 chapters)
- Download Preface 1 PDF (71.7 KB)
- Download Sample pages 2 PDF (376.1 KB)
- Download Table of contents PDF (73.2 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Deep Learning and Convolutional Neural Networks for Medical Image Computing
- Book Subtitle
- Precision Medicine, High Performance and Large-Scale Datasets
- Editors
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- Lu Le
- Yefeng Zheng
- Gustavo Carneiro
- Lin Yang
- Series Title
- Advances in Computer Vision and Pattern Recognition
- Copyright
- 2017
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing Switzerland
- eBook ISBN
- 978-3-319-42999-1
- DOI
- 10.1007/978-3-319-42999-1
- Hardcover ISBN
- 978-3-319-42998-4
- Softcover ISBN
- 978-3-319-82713-1
- Series ISSN
- 2191-6586
- Edition Number
- 1
- Number of Pages
- XIII, 326
- Number of Illustrations
- 17 b/w illustrations, 100 illustrations in colour
- Topics
