Artificial Intelligence in Pathology

Dr. Chhavi Chauhan works as Director for Scientific Outreach at the American Society for Investigative Pathology. She is one of the leaders of the Women in AI Ethics Collective and an expert at the AI Policy Exchange.  She is a biomedical researcher, expert scholarly communicator, and a sought-after mentor in the fields of scientific research, scholarly publishing, and AI Ethics, especially for women and minorities. She was honored to be featured in The AI Makers 150: top 150 AI &Analytics Leaders & Influencers 2021 list. She is a thought leader, a renowned international speaker, and a strong advocate for equitable and accessible healthcare.  She sits at the intersection of scientific research, scholarly communications, and AI Ethics in Healthcare.   Her vision is to provide equitable personalized healthcare to all, beyond geographies, and despite socioeconomic barriers. Dr. Cohen is currently interested in integrating computational imaging with digital workflows. He previously served as President of the American Society for Investigative Pathology (ASIP) and Treasurer and Member of the Executive Board of FASEB. Science-related activities also include chairmanships of study sections for the NIH and DOD and membership on multiple editorial boards. He is currently the Associate Editor for digital and computational pathology and artificial intelligence topic category for the American Journal of Pathology. He is a Senior Fellow of the Association of Pathology Chairs and Co-Chair of the ASIP Special Interest Group on Digital and Computational Pathology. Awards include the Gold-Headed Cane (ASIP) and the Golden Goose Award (AAAS). He is a member of the Digital Pathology Association (DPA), the Board of the International Academy of Digital Pathology (IADP), and Chair of the External Advisory Board of the Alpert Foundation.

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