Deep Learning for Medical Image Analysis 1st Edition by Kevin Zhou, Hayit Greenspan, Dinggang Shen 0128104082 9780128104088

Deep Learning for Medical Image Analysis 1st Edition by S. Kevin Zhou, Hayit Greenspan, Dinggang Shen – Ebook PDF Instant Download/DeliveryISBN:  0128104082, 9780128104088 

Full download Deep Learning for Medical Image Analysis 1st Edition after payment.

Product details:

ISBN-10 :  0128104082 

ISBN-13 : 9780128104088

Author:  S. Kevin Zhou, Hayit Greenspan, Dinggang Shen 

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 1st Table of contents:

Part I: Introduction

1. An Introduction to Neural Networks and Deep Learning

1.1. Introduction
1.2. Feed-Forward Neural Networks
1.3. Convolutional Neural Networks
1.4. Deep Models
1.5. Tricks for Better Learning
1.6. Open-Source Tools for Deep Learning

2. An Introduction to Deep Convolutional Neural Nets for Computer Vision

2.1. Introduction
2.2. Convolutional Neural Networks
2.3. CNN Flavors
2.4. Software for Deep Learning

Part II: Medical Image Detection and Recognition

3. Efficient Medical Image Parsing

3.1. Introduction
3.2. Background and Motivation
3.3. Methodology
3.4. Experiments
3.5. Conclusion

4. Multi-Instance Multi-Stage Deep Learning for Medical Image Recognition

4.1. Introduction
4.2. Related Work
4.3. Methodology
4.4. Results
4.5. Discussion and Future Work

5. Automatic Interpretation of Carotid Intima–Media Thickness Videos Using Convolutional Neural Networks

5.1. Introduction
5.2. Related Work
5.3. CIMT Protocol
5.4. Method
5.5. Experiments
5.6. Discussion
5.7. Conclusion

6. Deep Cascaded Networks for Sparsely Distributed Object Detection from Medical Images

6.1. Introduction
6.2. Method
6.3. Mitosis Detection from Histology Images
6.4. Cerebral Microbleed Detection from MR Volumes
6.5. Discussion and Conclusion

7. Deep Voting and Structured Regression for Microscopy Image Analysis

7.1. Deep Voting: A Robust Approach Toward Nucleus Localization in Microscopy Images
7.2. Structured Regression for Robust Cell Detection Using Convolutional Neural Network

Part III: Medical Image Segmentation

8. Deep Learning Tissue Segmentation in Cardiac Histopathology Images

8.1. Introduction
8.2. Experimental Design and Implementation
8.3. Results and Discussion
8.4. Concluding Remarks
Notes
Disclosure Statement
Funding
References
9. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

9.1. Background
9.2. Proposed Method
9.3. Experiments
9.4. Conclusion
References
10. Characterization of Errors in Deep Learning-Based Brain MRI Segmentation

10.1. Introduction
10.2. Deep Learning for Segmentation
10.3. Convolutional Neural Network Architecture
10.4. Experiments
10.5. Results
10.6. Discussion
10.7. Conclusion

Part IV: Medical Image Registration

11. Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning

11.1. Introduction
11.2. Proposed Method
11.3. Experiments
11.4. Conclusion
References
12. Convolutional Neural Networks for Robust and Real-Time 2-D/3-D Registration

12.1. Introduction
12.2. X-Ray Imaging Model
12.3. Problem Formulation
12.4. Regression Strategy
12.5. Feature Extraction
12.6. Convolutional Neural Network
12.7. Experiments and Results
12.8. Discussion
Disclaimer
References
Part V: Computer-Aided Diagnosis and Disease Quantification

13. Chest Radiograph Pathology Categorization via Transfer Learning

13.1. Introduction
13.2. Image Representation Schemes with Classical (Non-Deep) Features
13.3. Extracting Deep Features from a Pre-Trained CNN Model
13.4. Extending the Representation Using Feature Fusion and Selection
13.5. Experiments and Results
13.6. Conclusion
References
14. Deep Learning Models for Classifying Mammogram Exams Containing Unregistered Multi-View Images and Segmentation Maps of Lesions

14.1. Introduction
14.2. Literature Review
14.3. Methodology
14.4. Materials and Methods
14.5. Results
14.6. Discussion
14.7. Conclusion
References
15. Randomized Deep Learning Methods for Clinical Trial Enrichment and Design in Alzheimer’s Disease

15.1. Introduction
15.2. Background
15.3. Optimal Enrichment Criterion
15.4. Randomized Deep Networks
15.5. Experiments
15.6. Discussion
References
Part VI: Others

16. Deep Networks and Mutual Information Maximization for Cross-Modal Medical Image Synthesis16.1. Introduction
16.2. Supervised Synthesis Using Location-Sensitive Deep Network
16.3. Unsupervised Synthesis Using Mutual Information Maximization
16.4. Conclusions and Future Work
References
17. Natural Language Processing for Large-Scale Medical Image Analysis Using Deep LearningAcknowledgements
17.1. Introduction
17.2. Fundamentals of Natural Language Processing
17.3. Neural Language Models
17.4. Medical Lexicons
17.5. Predicting Presence or Absence of Frequent Disease Types
17.6. Conclusion
References

 

People also search for Deep Learning for Medical Image Analysis 1st:

deep learning for medical image analysis
    
advances in deep learning for medical image analysis
    
a review paper about deep learning for medical image analysis
    
data efficient deep learning for medical image analysis a survey
    
borrow deep learning for medical image analysis

Tags: Deep Learning, Medical Image, Kevin Zhou, Hayit Greenspan, Dinggang Shen