Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
Editors: Le, L., Wang, X., Carneiro, G., Yang, L. (Eds.)
Free Preview- 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
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- About this book
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This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases. It particularly focuses on the application of convolutional neural networks, and on recurrent neural networks like LSTM, using numerous practical examples to complement the theory.
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.
- About the authors
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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.
- Table of contents (21 chapters)
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Pancreas Segmentation in CT and MRI via Task-Specific Network Design and Recurrent Neural Contextual Learning
Pages 3-21
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Deep Learning for Muscle Pathology Image Analysis
Pages 23-41
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2D-Based Coarse-to-Fine Approaches for Small Target Segmentation in Abdominal CT Scans
Pages 43-67
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Volumetric Medical Image Segmentation: A 3D Deep Coarse-to-Fine Framework and Its Adversarial Examples
Pages 69-91
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Unsupervised Domain Adaptation of ConvNets for Medical Image Segmentation via Adversarial Learning
Pages 93-115
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Table of contents (21 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics
- Editors
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- Lu Le
- Xiaosong Wang
- Gustavo Carneiro
- Lin Yang
- Series Title
- Advances in Computer Vision and Pattern Recognition
- Copyright
- 2019
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-13969-8
- DOI
- 10.1007/978-3-030-13969-8
- Hardcover ISBN
- 978-3-030-13968-1
- Softcover ISBN
- 978-3-030-13971-1
- Series ISSN
- 2191-6586
- Edition Number
- 1
- Number of Pages
- XI, 461
- Number of Illustrations
- 21 b/w illustrations, 156 illustrations in colour
- Topics
