ARTIFICIAL INTELLIGENCE AND DEEP LEARNING IN PATHOLOGY

1. The evolution of machine learning: past, present, and future
2. The basics of machine learning: strategies and techniques
3. Overview of advanced neural network architectures
4. Complexity in the use of artificial intelligence in anatomic pathology
5. Dealing with data: strategies of preprocessing data
6. Digital pathology as a platform for primary diagnosis and augmentation via deep learning.
7. Applications of artificial intelligence for image enhancement in pathology
8. Precision medicine in digital pathology via image analysis and machine learning
9. Artificial intelligence methods for predictive image-based grading of human cancers
10. Artificial intelligence and the interplay between tumor and immunity
11. Overview of the role of artificial intelligence in pathology: the computer as a pathology digital assistant

Recent advances in computational algorithms, along with the advent of whole slide imaging as a platform for embedding artificial intelligence (AI), are transforming pattern recognition and image interpretation for diagnosis and prognosis. Yet most pathologists have just a passing knowledge of data mining, machine learning, and AI, and little exposure to the vast potential of these powerful new tools for medicine in general and pathology in particular. In Artificial Intelligence and Deep Learning in Pathology, Dr. Dr. Stanley Cohen, with a team of experts, covers the nuts and bolts of all aspects of machine learning, up to and including AI, bringing familiarity and understanding to pathologists at all levels of experience.

Author
Stanley Cohen, MD, Emeritus Founding Director, Center for Biophysical Pathology, Rutgers-NJMS; Adjunct Professor of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Perelman School of Medicine at the University of Pennsylvania, Sidney Kimmell Medical College - Thomas Jefferson University, Philadelphia, Pennsylvania