ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING : opportunities, applications

Intro; I've Seen the Future ...; Preface; Contents; Part I Introduction; 1 Introduction: Game Changers in Radiology; 1.1 Era of Changes; 1.2 Perspectives; 1.3 Opportunities for the Future; 1.4 Conclusion; Reference; Part II Technology: Getting Started; 2 The Role of Medical Image Computing and Machine Learning in Healthcare; 2.1 Introduction; 2.2 Medical Image Analysis; 2.2.1 Image Segmentation; 2.2.2 Image Registration; 2.2.3 Image Visualization; 2.3 Challenges; 2.3.1 Complexity of the Data; 2.3.2 Complexity of the Objects of Interest; 2.3.3 Complexity of the Validation 2.4 Medical Image Computing2.5 Model-Based Image Analysis; 2.5.1 Energy Minimization; 2.5.2 Classification/Regression; 2.6 Computational Strategies; 2.6.1 Flexible Shape Fitting; 2.6.2 Pixel Classification; 2.7 Fundamental Issues; 2.7.1 Explicit Versus Implicit Representation of Geometry; 2.7.2 Global Versus Local Representations of Appearance; 2.7.3 Deterministic Versus Statistical Models; 2.7.4 Data Congruency Versus Model Fidelity; 2.8 Conclusion; References; 3 A Deeper Understanding of Deep Learning; 3.1 Introduction; 3.2 Computer-Aided Diagnosis, the Classical Approaches 3.3 Artificial Intelligence3.4 Neural Networks; 3.5 Convolutional Neural Networks; 3.6 Why Now?; 3.7 Example: Screening for Diabetic Retinopathy; 3.8 Pointers on the Web; 3.9 A Comparison with Brain Research; 3.9.1 Brain Efficiency; 3.9.2 Visual Learning; 3.9.3 Foveated Vision; 3.10 Conclusions and Recommendations; 3.11 Take Home Messages; References; 4 Deep Learning and Machine Learning in Imaging: Basic Principles; 4.1 Introduction; 4.2 Features and Classes; 4.3 Neural Networks; 4.4 Support Vector Machines; 4.5 Decision Trees; 4.6 Bayes Network; 4.7 Deep Learning; 4.7.1 Deep Learning Layers 4.7.2 Deep Learning Architectures4.8 Conclusion; References; Part III Technology: Developing A.I. Applications; 5 How to Develop Artificial Intelligence Applications; 5.1 Introduction; 5.2 Applications of AI in Radiology; 5.3 Development of AI Applications in Radiology; 5.4 Resources Framework; 5.5 Conclusion; 5.6 Summary/Take-Home Points; References; 6 A Standardised Approach for Preparing Imaging Data for Machine Learning Tasks in Radiology; 6.1 Data, Data Everywhere?; 6.2 Not All Data Is Created Equal; 6.3 The MIDaR Scale; 6.3.1 MIDaR Level D; 6.3.2 MIDaR Level C; 6.3.3 MIDaR Level B 6.3.4 MIDaR Level A6.4 Summary; 6.5 Take Home Points; References; 7 The Value of Structured Reporting for AI; 7.1 Introduction; 7.2 Conventional Radiological Reporting Versus Structured Reporting; 7.3 Technical Implementations of Structured Reporting and IHE MRRT; 7.4 Information Extraction Using Natural Language Processing; 7.5 Information Extraction from Structured Reports; 7.6 Integration of External Data into Structured Reports; 7.7 Analytics and Clinical Decision Support; 7.8 Outlook; References; 8 Artificial Intelligence in Medicine: Validation and Study Design