Artificial Intelligence in Pathology

Name: Artificial Intelligence in Pathology
Price: 165 USD
Availability: InStock
ISBN: 9780323953597

Principles and Applications

by Stanley Cohen, Chhavi Chauhan

2nd Edition

Publisher: Elsevier

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Book Details

Print ISBN	9780323953597
eText ISBN	9780323958325
Publisher	Elsevier
Publishing Year	2024
Edition	2nd Edition
Language	English
Pages	486

Artificial Intelligence in Pathology: Principles and Applications, 2nd Edition establishes core computational concepts alongside their direct implementation in diagnostic environments. Written by Stanley Cohen and Chhavi Chauhan, this reference work presents technical frameworks for academicians, pathology researchers, and practicing clinicians. It demonstrates how computational tools integrate with laboratory workflows to support diagnostic evaluation.

The volume surveys early machine learning strategies before detailing advanced neural network architectures. It addresses data pre-processing methods, systematic techniques for easing annotation burdens, and deep learning models designed for primary diagnostic augmentation. The text also analyzes ethical paradigms and emerging deployment challenges across regulatory, technical, and organizational boundaries.

This second edition features generative deep learning workflows within active pathology operations as a defining analytical component. The resulting synthesis assists healthcare administrators, health policymakers, and medical vendors who evaluate model scalability, adoption obstacles, and governance standards in pathology practice.

Chapter 1: The evolution of machine learning
Chapter 2: Basics of machine learning strategies
Chapter 3: Overview of advanced neural network architectures
Chapter 4: Complexity in the use of AI in anatomic pathology
Chapter 5: Quantum Artificial Intelligence: Things to come
Chapter 6: Dealing with data: strategies for pre-processing
Chapter 7: Easing the Burden of Annotation in pathology
Chapter 8: Digital path as a platform for primary diagnosis and augmentation via a deep learning
Chapter 9: Challenges in the Development, Deployment, and Regulation of AI in Anatomic Pathology
Chapter 10: Ethics of AI in Pathology: Current Paradigms and Emerging Issues
Chapter 11: Image enhancement via AI
Chapter 12: Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images
Chapter 13: Precision medicine in digital pathology
Chapter 14: Generative Deep Learning in Digital Pathology Workflows
Chapter 15: Predictive image-based grading of human cancer
Chapter 16: The interplay between tumor and immunity
Chapter 17: Machine-based evaluation intra-tumoral heterogeneity and tumor-stromal interface
Chapter 18: The computer as digital pathology assistant
Chapter 19: Neuromorphic computing, general AI, and the future of pathology