BookErik R. Ranschaert, Sergey Morozov, Paul R. Algra, editors.
Summary: This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Contents:
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