Artificial Intelligence in Pathology: Principles and Applications

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, policy makers, 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. Discusses the evolution of machine learning in the field to provide a foundational background Addresses challenges in the development, deployment, and regulation of AI in anatomic pathology Includes information on generative deep learning in digital pathology workflows Provides current tools and future INDICE: PART I PRINCIPLES 1. The evolution of machine learning 2. Basics of machine learning strategies 3. Overview of advanced neural network architectures 4. Complexity in the use of AI in anatomic pathology 5. Quantum Artificial Intelligence: Things to come 6. Dealing with data: strategies for pre-processing 7. Easing the Burden of Annotation in pathology 8. Digital path as a platform for primary diagnosis and augmentation via a deep learning 9. Challenges in the Development, Deployment, and Regulation of AI in Anatomic Pathology 10. Ethics of AI in Pathology: Current Paradigms and Emerging Issues PART II APPLICATIONS 11. Image enhancement via AI 12. Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images 13. Precision medicine in digital pathology 14. Generative Deep Learning in Digital Pathology Workflows 15. Predictive image-based grading of human cancer 16. The interplay between tumor and immunity 17. Machine-based evaluation intra-tumoral heterogeneity and tumor-stromal interface PART III OVERVIEW 18. The computer as digital pathology assistant 19. Neuromorphic computing, general AI, and the future of pathology

• ISBN: 978-0-323-95359-7
• Editorial: Elsevier
• Encuadernacion: Rústica
• Páginas: 500
• Fecha Publicación: 01/07/2023
• Nº Volúmenes: 1
• Idioma: Inglés