Overview
- Reviews the state of the art in deep learning approaches to robust disease detection, organ segmentation in medical image computing, and the construction and mining of large-scale radiology databases
- Particularly focuses on the application of convolutional neural networks, supporting the theory with numerous practical examples
- Highlights how deep neural networks can be used to address new questions and protocols, and provide novel solutions
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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Table of contents (21 chapters)
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Front Matter
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Segmentation
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Front Matter
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Detection and Localization
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Front Matter
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Various Applications
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Front Matter
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Keywords
- Deep Learning
- Convolutional Neural Networks
- Medical Image Analytics
- Computer-Aided Diagnosis
- Hospital-Scale Imaging Data Process
- Disease Detection
- Organ Segmentation
- Medical Image Computing
- Radiology Database Construction and Mining
- Object and Landmark Detection
- 2D and 3D Medical Imaging
- Semantic Segmentation
- Text and Image Deep Embedding
- Learning Deep Relational Graphs
- Semantic Similarity-Based Retrieval
About this book
The book’s chief features are as follows: It highlights how deep neural networks can be used to address new questions and protocols, and to tackle current challenges in medical image computing; presents a comprehensive review of the latest research and literature; and describes a range of different methods that employ deep learning for object or landmark detection tasks in 2D and 3D medical imaging. In addition, the book examines a broad selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to text and image deep embedding for a large-scale chest x-ray image database; and discusses how deep learning relational graphs can be used to organize a sizable collection of radiology findings from real clinical practice, allowing semantic similarity-based retrieval.
The intended reader of this edited book is a professional engineer, scientist or a graduate student who is able to comprehend general concepts of image processing, computer vision and medical image analysis. They can apply computer science and mathematical principles into problem solving practices. It may be necessary to have a certain level of familiarity with a number of more advanced subjects: image formation and enhancement, image understanding, visual recognition in medical applications, statistical learning, deep neural networks, structured prediction and image segmentation.
Editors and Affiliations
About the editors
Dr. Le Lu is the Director of Ping An Technology US Research Labs, and an adjunct faculty member at Johns Hopkins University, USA.
Dr. Xiaosong Wang is a Senior Applied Research Scientist at Nvidia Corp., USA.
Dr. Gustavo Carneiro is an Associate Professor at the University of Adelaide, Australia.
Dr. Lin Yang is an Associate Professor at the University of Florida, USA.
Bibliographic Information
Book Title: Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
Editors: Le Lu, Xiaosong Wang, Gustavo Carneiro, Lin Yang
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-3-030-13969-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-13968-1Published: 01 October 2019
Softcover ISBN: 978-3-030-13971-1Published: 01 October 2020
eBook ISBN: 978-3-030-13969-8Published: 19 September 2019
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: XI, 461
Number of Illustrations: 21 b/w illustrations, 156 illustrations in colour
Topics: Image Processing and Computer Vision, Imaging / Radiology, Artificial Intelligence, Mathematical Models of Cognitive Processes and Neural Networks