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Deep Learning and Convolutional Neural Networks for Medical Image Computing : Precision Medicine, High Performance and Large-Scale Datasets /

This timely text/reference 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 netw...

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Bibliographic Details
Corporate Authors: SpringerLink Online service
Group Author: Lu, Le; Zheng, Yefeng; Carneiro, Gustavo; Yang, Lin
Published: Springer International Publishing : Imprint: Springer,
Publisher Address: Cham :
Publication Dates: 2017.
Literature type: eBook
Language: English
Series: Advances in Computer Vision and Pattern Recognition,
Subjects:
Computer science
Radiology
Artificial intelligence
Image processing
Neural networks Computer science
Image Processing and Computer Vision
Artificial Intelligence incl. Robotics
Mathematical Models of Cognitive Processes and Neural Networks
Imaging / Radiology
Online Access: http://dx.doi.org/10.1007/978-3-319-42999-1
Summary: This timely text/reference 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. Topics and 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 and views of Dr. R
Carrier Form: 1 online resource(xiii,326pages): illustrations.
ISBN: 9783319429991
Index Number: TA1637
CLC: TP183
Contents: Part I: Review -- Chapter 1. Deep Learning and Computer-Aided Diagnosis for Medical Image Processing: A Personal Perspective -- Chapter 2. Review of Deep Learning Methods in Mammography, Cardiovascular and Microscopy Image Analysis -- Part II: Detection and Localization -- Chapter 3. Efficient False-Positive Reduction in Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation -- Chapter 4. Robust Landmark Detection in Volumetric Data with Efficient 3D Deep Learning -- Chapter 5. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum

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