Introduction: The Nature of Artificial Intelligence: Machine Learning and Deep
Learning in Digital Pathology
Before Deep Learning: Statistical Analysis and Signal Processing, Classical Machine
(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
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
Summary and Overview: Emerging New Imaging Technologies and The Rise of the
Machine: Human vs Computer capabilities
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.
- 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.
Edited by Stanley Cohen, MD, Emeritus Founding Director, Center for Biophysical Pathology, Rutgers-NJMS Adjunct Professor of Pathology Feinberg Med. Sch., Northwestern U. Perelman Med. Sch., U. Penn., & Kimmel Sch. of Medicine, Jefferson U.