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Deep learning and convolutional neural networks for medical image computing: precision medicine, high performance and large scale datasets

Contributor(s): Lu, Le [Editor] | Zheng, Yefeng [Editor] | Carneiro, Gustavo [Editor] | Yang, Lin [Editor].
Publisher: Switzerland Springer 2017Description: xiii, 326 p.ISBN: 9783319429984.Subject(s): Neural networks- Computer science | Diagnostic imaging- Data processing | Radiology | Computer science | Image processingDDC classification: 006.32 Summary: 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. https://www.springer.com/in/book/9783319429984
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Non-fiction 006.32 D3 (Browse shelf) Checked out 10/09/2019 197150

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.

https://www.springer.com/in/book/9783319429984

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