Artificial Intelligence and Deep Learning in Pathology

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.

1. The evolution of machine learning: past, present, and future

Stanley Cohen

2. The basics of machine learning: strategies and techniques

Stanley Cohen

3. Overview of advanced neural network architectures

Benjamin R. Mitchell

4. Complexity in the use of artificial intelligence in anatomic pathology

Stanley Cohen

5. Dealing with data: strategies of preprocessing data

Stanley Cohen

6. Digital pathology as a platform for primary diagnosis and augmentation via deep learning.

Anil V. Parwani

7. Applications of artificial intelligence for image enhancement in pathology

Tanishq Abraham, Austin Todd, Daniel A. Orringer and Richard Levenson

8. Precision medicine in digital pathology via image analysis and machine learning

Peter D. Caie, Neofytos Dimitriou and Ognjen Arandjelovi'c

9. Artificial intelligence methods for predictive image-based grading of human cancers

Gerardo Fernandez, Abishek Sainath Madduri, Bahram Marami, Marcel Prastawa, Richard Scott, Jack Zeineh and Michael Donovan

10. Artificial intelligence and the interplay between tumor and immunity

Joel Haskin Saltz and Rajarsi Gupta

11. Overview of the role of artificial intelligence in pathology: the computer as a pathology digital assistant