Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets
Language: English
Published by Springer, 2017
Series: Book 63 of 86 - Advances in Computer Vision and Pattern Recognition
- Hardcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
Druck auf Anfrage Neuware - Printed after ordering - 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.…
Seller Inventory # 9783319429984
- Title
- Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets
- Author
- Lin Yang
- Publisher
- Springer
- Publication year
- 2017
- Condition
- Neu
- Binding
- Buch
- Language
- English
- ISBN 10
- 3319429981
- ISBN 13
- 9783319429984
- Item weight
- 738 grams
- Dimensions
- 241x160x23 mm
- Series
- Book 63 of 86: Advances in Computer Vision and Pattern Recognition
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.
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