Coverart for item
The Resource Deep learning for medical image analysis, edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

Deep learning for medical image analysis, edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen

Label
Deep learning for medical image analysis
Title
Deep learning for medical image analysis
Statement of responsibility
edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
Contributor
Editor
Subject
Language
eng
Summary
"Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis"--
Member of
Assigning source
provided by publisher
Cataloging source
N$T
Dewey number
616.07/54
Index
index present
LC call number
RC78.7.D53
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
NLM call number
WN 180
http://library.link/vocab/relatedWorkOrContributorName
  • Zhou, S. Kevin
  • Greenspan, Hayit
  • Shen, Dinggang
Series statement
The Elsevier and MICCAI Society book series
http://library.link/vocab/subjectName
  • Diagnostic imaging
  • Image analysis
  • Diagnostic Imaging
  • MEDICAL
  • Diagnostic imaging
  • Image analysis
Label
Deep learning for medical image analysis, edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Front Cover; Deep Learning for Medical Image Analysis; Copyright; Contents; Contributors; About the Editors; Foreword; Part 1 Introduction; 1 An Introduction to Neural Networks and Deep Learning; 1.1 Introduction; 1.2 Feed-Forward Neural Networks; 1.2.1 Perceptron; 1.2.2 Multi-Layer Neural Network; 1.2.3 Learning in Feed-Forward Neural Networks; 1.3 Convolutional Neural Networks; 1.3.1 Convolution and Pooling Layer; 1.3.2 Computing Gradients; 1.4 Deep Models; 1.4.1 Vanishing Gradient Problem; 1.4.2 Deep Neural Networks; 1.4.3 Deep Generative Models; 1.5 Tricks for Better Learning
  • 1.5.1 Rectified Linear Unit (ReLU)1.5.2 Dropout; 1.5.3 Batch Normalization; 1.6 Open-Source Tools for Deep Learning; References; Notes; 2 An Introduction to Deep Convolutional Neural Nets for Computer Vision; 2.1 Introduction; 2.2 Convolutional Neural Networks; 2.2.1 Building Blocks of CNNs; 2.2.2 Depth; 2.2.3 Learning Algorithm; 2.2.4 Tricks to Increase Performance; 2.2.5 Putting It All Together: AlexNet; 2.2.6 Using Pre-Trained CNNs; 2.2.7 Improving AlexNet; 2.3 CNN Flavors; 2.3.1 Region-Based CNNs; 2.3.2 Fully Convolutional Networks; 2.3.3 Multi-Modal Networks; 2.3.4 CNNs with RNNs
  • 2.3.5 Hybrid Learning Methods2.4 Software for Deep Learning; References; Part 2 Medical Image Detection and Recognition; 3 Efficient Medical Image Parsing; 3.1 Introduction; 3.2 Background and Motivation; 3.2.1 Object Localization and Segmentation: Challenges; 3.3 Methodology; 3.3.1 Problem Formulation; 3.3.2 Sparse Adaptive Deep Neural Networks; 3.3.3 Marginal Space Deep Learning; 3.3.4 An Artificial Agent for Image Parsing; 3.4 Experiments; 3.4.1 Anatomy Detection and Segmentation in 3D; 3.4.2 Landmark Detection in 2D and 3D; 3.5 Conclusion; Disclaimer; References
  • 4 Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition4.1 Introduction; 4.2 Related Work; 4.3 Methodology; 4.3.1 Problem Statement and Framework Overview; 4.3.2 Learning Stage I: Multi-Instance CNN Pre-Train; 4.3.3 Learning Stage II: CNN Boosting; 4.3.4 Run-Time Classification; 4.4 Results; 4.4.1 Image Classification on Synthetic Data; 4.4.2 Body-Part Recognition on CT Slices; 4.5 Discussion and Future Work; References; 5 Automatic Interpretation of Carotid Intima-Media Thickness Videos Using Convolutional Neural Networks; 5.1 Introduction; 5.2 Related Work
  • 5.3 CIMT Protocol5.4 Method; 5.4.1 Convolutional Neural Networks (CNNs); 5.4.2 Frame Selection; 5.4.3 ROI Localization; 5.4.4 Intima-Media Thickness Measurement; 5.5 Experiments; 5.5.1 Pre- and Post-Processing for Frame Selection; 5.5.2 Constrained ROI Localization; 5.5.3 Intima-Media Thickness Measurement; 5.5.4 End-to-End CIMT Measurement; 5.6 Discussion; 5.7 Conclusion; Acknowledgement; References; Notes; 6 Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images; 6.1 Introduction; 6.2 Method; 6.2.1 Coarse Retrieval Model; 6.2.2 Fine Discrimination Model
Control code
970041768
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9780128104095
Lccn
2016960969
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000838
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)970041768
Label
Deep learning for medical image analysis, edited by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Front Cover; Deep Learning for Medical Image Analysis; Copyright; Contents; Contributors; About the Editors; Foreword; Part 1 Introduction; 1 An Introduction to Neural Networks and Deep Learning; 1.1 Introduction; 1.2 Feed-Forward Neural Networks; 1.2.1 Perceptron; 1.2.2 Multi-Layer Neural Network; 1.2.3 Learning in Feed-Forward Neural Networks; 1.3 Convolutional Neural Networks; 1.3.1 Convolution and Pooling Layer; 1.3.2 Computing Gradients; 1.4 Deep Models; 1.4.1 Vanishing Gradient Problem; 1.4.2 Deep Neural Networks; 1.4.3 Deep Generative Models; 1.5 Tricks for Better Learning
  • 1.5.1 Rectified Linear Unit (ReLU)1.5.2 Dropout; 1.5.3 Batch Normalization; 1.6 Open-Source Tools for Deep Learning; References; Notes; 2 An Introduction to Deep Convolutional Neural Nets for Computer Vision; 2.1 Introduction; 2.2 Convolutional Neural Networks; 2.2.1 Building Blocks of CNNs; 2.2.2 Depth; 2.2.3 Learning Algorithm; 2.2.4 Tricks to Increase Performance; 2.2.5 Putting It All Together: AlexNet; 2.2.6 Using Pre-Trained CNNs; 2.2.7 Improving AlexNet; 2.3 CNN Flavors; 2.3.1 Region-Based CNNs; 2.3.2 Fully Convolutional Networks; 2.3.3 Multi-Modal Networks; 2.3.4 CNNs with RNNs
  • 2.3.5 Hybrid Learning Methods2.4 Software for Deep Learning; References; Part 2 Medical Image Detection and Recognition; 3 Efficient Medical Image Parsing; 3.1 Introduction; 3.2 Background and Motivation; 3.2.1 Object Localization and Segmentation: Challenges; 3.3 Methodology; 3.3.1 Problem Formulation; 3.3.2 Sparse Adaptive Deep Neural Networks; 3.3.3 Marginal Space Deep Learning; 3.3.4 An Artificial Agent for Image Parsing; 3.4 Experiments; 3.4.1 Anatomy Detection and Segmentation in 3D; 3.4.2 Landmark Detection in 2D and 3D; 3.5 Conclusion; Disclaimer; References
  • 4 Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition4.1 Introduction; 4.2 Related Work; 4.3 Methodology; 4.3.1 Problem Statement and Framework Overview; 4.3.2 Learning Stage I: Multi-Instance CNN Pre-Train; 4.3.3 Learning Stage II: CNN Boosting; 4.3.4 Run-Time Classification; 4.4 Results; 4.4.1 Image Classification on Synthetic Data; 4.4.2 Body-Part Recognition on CT Slices; 4.5 Discussion and Future Work; References; 5 Automatic Interpretation of Carotid Intima-Media Thickness Videos Using Convolutional Neural Networks; 5.1 Introduction; 5.2 Related Work
  • 5.3 CIMT Protocol5.4 Method; 5.4.1 Convolutional Neural Networks (CNNs); 5.4.2 Frame Selection; 5.4.3 ROI Localization; 5.4.4 Intima-Media Thickness Measurement; 5.5 Experiments; 5.5.1 Pre- and Post-Processing for Frame Selection; 5.5.2 Constrained ROI Localization; 5.5.3 Intima-Media Thickness Measurement; 5.5.4 End-to-End CIMT Measurement; 5.6 Discussion; 5.7 Conclusion; Acknowledgement; References; Notes; 6 Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images; 6.1 Introduction; 6.2 Method; 6.2.1 Coarse Retrieval Model; 6.2.2 Fine Discrimination Model
Control code
970041768
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9780128104095
Lccn
2016960969
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
http://library.link/vocab/ext/overdrive/overdriveId
cl0500000838
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)970041768

Library Locations

    • Ellis LibraryBorrow it
      1020 Lowry Street, Columbia, MO, 65201, US
      38.944491 -92.326012
    • Engineering Library & Technology CommonsBorrow it
      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
Processing Feedback ...