Artificial Intelligence in Pathology: Principles and Applications (Paperback)

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