Deep learning for medical image analysis
"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 dee...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
London, United Kingdom
Academic Press is an imprint of Elsevier
2017
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| Series: | The Elsevier and MICCAI Society book series
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| Subjects: | |
| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Includes bibliographical references and index
- 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
- 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
- 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
- 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
- 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