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.
%0 Book
%1 LuZhengEtAl2017
%B Advances in Computer Vision and Pattern Recognition
%C Cham
%D 2017
%E Lu, Le
%E Zheng, Yefeng
%E Carneiro, Gustavo
%E Yang, Lin
%I Springer
%K 02001 103 4rda 8book 9springer ai analysis data health image learn neuro numerical pattern recognition video
%R 10.1007/978-3-319-42999-1
%T Deep Learning and Convolutional Neural Networks for Medical Image Computing
%X 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.
%@ 978-3-319-42998-4
@book{LuZhengEtAl2017,
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abstract = {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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doi = {10.1007/978-3-319-42999-1},
editor = {Lu, Le and Zheng, Yefeng and Carneiro, Gustavo and Yang, Lin},
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pagetotal = {326},
publisher = {Springer},
referencetype = {collection},
series = {Advances in Computer Vision and Pattern Recognition},
subtitle = {Precision Medicine, High Performance and Large-Scale Datasets},
timestamp = {2019-04-13T19:49:31.000+0200},
title = {Deep Learning and Convolutional Neural Networks for Medical Image Computing},
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x.sortdate = {2017-08-01},
year = 2017
}