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| Additional Physical Format: | (OCoLC)1037809048 |
|---|---|
| Material Type: | Document, Internet resource |
| Document Type: | Internet Resource, Computer File |
| ISBN: | 9783319948782 3319948784 3319948776 9783319948775 |
| OCLC Number: | 1117884444 |
| Description: | 1 online resource |
| 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 |
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Publisher Synopsis
"The book seems practical and interesting for newcomers to the feld and also experts. This book covers a range of introductory to advanced issues of AI and can respond well to the concerns of researchers. The presented examples ... prepare the ground for familiarity with the research process and future research trends in this feld. Based on the reviews, we can recommend this book to researchers as a desirable book as a gateway to enter this feld." (Shahabedin Nabavi and Mohammad Mohammadi, Physical and Engineering Sciences in Medicine, Vol. 44, 2021) Read more...
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Related Subjects:(11)
- Diagnostic imaging -- Data processing.
- Artificial intelligence -- Medical applications.
- Diagnostic imaging.
- Artificial intelligence.
- Diagnostic Imaging
- Artificial Intelligence
- Imagerie pour le diagnostic -- Informatique.
- Intelligence artificielle en médecine.
- Imagerie pour le diagnostic.
- Intelligence artificielle.
- artificial intelligence.
