Artificial Intelligence and Deep Learning in Pathology (Paperback)

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