Artificial Intelligence in Pathology

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