Artificial Intelligence in Pathology: Principles and Applications provides a strong foundation of core artificial intelligence principles and their applications in the field of digital pathology. This is a reference of current and emerging use of AI in digital pathology as well as the emerging utility of quantum artificial intelligence and neuromorphic computing in digital pathology. It is a must-have educational resource for lay public, researchers, academicians, practitioners, policymakers, key administrators, and vendors to stay current with the shifting landscapes within the emerging field of digital pathology. It is also of use to workers in other diagnostic imaging areas such as radiology. This resource covers various aspects of the use of AI in pathology, including but not limited to the basic principles, advanced applications, challenges in the development, deployment, adoption, and scalability of AI-based models in pathology, the innumerous benefits of applying and integrating AI in the practice of pathology, ethical considerations for the safe adoption and deployment of AI in pathology.
1. Title of Book - p. Cover 2. Cover image - p. Cover 3. Title page - p. ii 4. Table of Contents - p. iv 5. Front Matter - p. i 6. Copyright - p. iii 7. Contributors - p. xv 8. Preface - p. xix 9. Acknowledgments - p. xx 10. Part I: Principles - p. xx 11. Chapter 1. The evolution of machine learning: Past, present, and future - p. 3 12. Chapter 2. The basics of machine learning: Strategies and techniques - p. 15 13. Chapter 3. Overview of advanced neural network architectures - p. 43 14. Chapter 4. Complexity in the use of artificial intelligence in anatomic pathology - p. 59 15. Chapter 5. Dealing with data: Strategies of preprocessing data - p. 79 16. Chapter 6. Artificial intelligence in pathology: Easing the burden of annotation - p. 95 17. Chapter 7. Digital pathology as a platform for primary diagnosis and augmentation via deep learning - p. 109 18. Chapter 8. Artificial intelligence model development, deployment, and regulatory challenges in anatomic pathology - p. 137 19. Chapter 9. Ethics of AI in pathology: Current paradigms and emerging issues - p. 161 20. Part II: Applications - p. 180 21. Chapter 10. Applications of artificial intelligence for image enhancement in pathology - p. 183 22. Chapter 11. Foundation models and information retrieval in digital pathology - p. 211 23. Chapter 12. Precision medicine in digital pathology via image analysis and machine learning - p. 233 24. Chapter 13. Generative deep learning in digital pathology - p. 259 25. Chapter 14. Artificial intelligence methods for predictive image-based grading of human cancers - p. 273 26. Chapter 15. Artificial intelligence and the interplay between cancer and immunity - p. 309 27. Chapter 16. Overview of the role of artificial intelligence in pathology: The computer as a pathology digital assistant - p. 343 28. Chapter 17. Overview and coda: The future of AI - p. 369 29. Index - p. 379
Artificial Intelligence in Pathology: Principles and Applications provides a strong foundation of core artificial intelligence principles and their applications in the field of digital pathology. This is a reference of current and emerging use of AI in digital pathology as well as the emerging utility of quantum artificial intelligence and neuromorphic computing in digital pathology. It is a must-have educational resource for lay public, researchers, academicians, practitioners, policymakers, key administrators, and vendors to stay current with the shifting landscapes within the emerging field of digital pathology. It is also of use to workers in other diagnostic imaging areas such as radiology. This resource covers various aspects of the use of AI in pathology, including but not limited to the basic principles, advanced applications, challenges in the development, deployment, adoption, and scalability of AI-based models in pathology, the innumerous benefits of applying and integrating AI in the practice of pathology, ethical considerations for the safe adoption and deployment of AI in pathology.
1. Title of Book - p. Cover 2. Cover image - p. Cover 3. Title page - p. ii 4. Table of Contents - p. iv 5. Front Matter - p. i 6. Copyright - p. iii 7. Contributors - p. xv 8. Preface - p. xix 9. Acknowledgments - p. xx 10. Part I: Principles - p. xx 11. Chapter 1. The evolution of machine learning: Past, present, and future - p. 3 12. Chapter 2. The basics of machine learning: Strategies and techniques - p. 15 13. Chapter 3. Overview of advanced neural network architectures - p. 43 14. Chapter 4. Complexity in the use of artificial intelligence in anatomic pathology - p. 59 15. Chapter 5. Dealing with data: Strategies of preprocessing data - p. 79 16. Chapter 6. Artificial intelligence in pathology: Easing the burden of annotation - p. 95 17. Chapter 7. Digital pathology as a platform for primary diagnosis and augmentation via deep learning - p. 109 18. Chapter 8. Artificial intelligence model development, deployment, and regulatory challenges in anatomic pathology - p. 137 19. Chapter 9. Ethics of AI in pathology: Current paradigms and emerging issues - p. 161 20. Part II: Applications - p. 180 21. Chapter 10. Applications of artificial intelligence for image enhancement in pathology - p. 183 22. Chapter 11. Foundation models and information retrieval in digital pathology - p. 211 23. Chapter 12. Precision medicine in digital pathology via image analysis and machine learning - p. 233 24. Chapter 13. Generative deep learning in digital pathology - p. 259 25. Chapter 14. Artificial intelligence methods for predictive image-based grading of human cancers - p. 273 26. Chapter 15. Artificial intelligence and the interplay between cancer and immunity - p. 309 27. Chapter 16. Overview of the role of artificial intelligence in pathology: The computer as a pathology digital assistant - p. 343 28. Chapter 17. Overview and coda: The future of AI - p. 369 29. Index - p. 379
