Deep Learning and Convolutional Neural Networks for Medical Image Computing
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Author Description
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.
Menrva's Summary
THE THESIS
This book investigates the application of deep learning, particularly convolutional neural networks, to complex challenges in medical image analysis and large-scale radiology database mining. It details state-of-the-art methods for semantic object detection and segmentation, offering practical examples to support theoretical concepts. The work highlights how deep neural networks can tackle novel research questions and improve existing medical imaging protocols, drawing on the research experience of Dr. Ronald M. Summers.
THE SCOPE MAP
- State-of-the-art deep learning approaches for semantic object detection and segmentation in medical imaging.
- Applications of convolutional neural networks with practical examples.
- Methods for object or landmark detection in 2D and 3D medical imaging.
- Techniques for semantic segmentation using deep learning in medical imaging.
- A novel approach to interleaved text and image deep mining on large-scale radiology databases.
- Discussion on how deep neural networks address new questions and improve existing challenges in medical image computing.
- Insights from the research experience of Dr. Ronald M. Summers.
THE AUTHORITY PERSPECTIVE
The book is presented as a comprehensive review of the latest research and literature in deep learning for medical image computing. It aims to provide readers with a detailed understanding of how deep neural networks can be applied to solve specific problems in radiology and image analysis. The inclusion of practical examples and research insights suggests a focus on both theoretical understanding and applied methodology for researchers and practitioners in the field.
Page Count:
326
Publication Date:
2017-07-12
Publisher:
Springer Nature
ISBN-10:
331942999X
ISBN-13:
9783319429991
Non-Fiction
Science
Databases
Computer Vision
Radiology
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