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. Stanley Cohen 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. - Focuses heavily on applications in medicine, especially pathology, making unfamiliar material accessible and avoiding complex mathematics whenever possible. - Covers digital pathology as a platform for primary diagnosis and augmentation via deep learning, whole slide imaging for 2D and 3D analysis, and general principles of image analysis and deep learning. - Discusses and explains recent accomplishments such as algorithms used to diagnose skin cancer from photographs, AI-based platforms developed to identify lesions of the retina, using computer vision to interpret electrocardiograms, identifying mitoses in cancer using learning algorithms vs. signal processing algorithms, and many more.

Introduction: The Nature of Artificial Intelligence: Machine Learning and Deep Learning in Digital Pathology

Before Deep Learning: Statistical Analysis and Signal Processing, Classical Machine Learning

(Geometric, Probabilistic, and Tree Methods, the Perceptron)

Whole Slide Imaging for 2D and 3D analysis: techniques and standardization

Digital Pathology as a Platform for Primary Diagnosis and Augmentation via Deep Learning

Introductory Deep Learning: Convolutional Neural Networks for Extraction, Classification and Prediction from images

Advanced Neural networks

(Reinforcement, Generative and Genetic Models, Variational encoders, Attention and Memory Networks, Deep Belief Networks)

AI Methods for Grading Human Cancers

Multilabel Classification (CNN-RNN), Prediction, and Risk Analysis

Advances in AI for Pathologists: Petascale AI, Data Warehousing and Repositories

Progress in Sparsely Supervised & Unsupervised Learning

Overview of the Role of AI in Anatomic Pathology: The Computer as Pathology Assistant

Summary and Overview: Emerging New Imaging Technologies and The Rise of the Machine: Human vs Computer capabilities