Artificial Intelligence in Pathology, 2nd Edition - Principles and Applications (24년 6월 출간예정)

※ 도서이미지는 추후 업데이트 예정

Editors: Stanley Cohen, Chhavi Chauhan

ISBN: 9780323953597

Imprint: Elsevier

Published: June, 2024

Pages: 500

Paperback 

Description

Artificial Intelligence in Pathology: Principles and Applications provides a strong foundation of core artificial intelligence principles and their applications in the field of digital pathology. This is a reference of current and emerging use of AI in digital pathology as well as the emerging utility of quantum artificial intelligence and neuromorphic computing in digital pathology. It is a must-have educational resource for lay public, researchers, academicians, practitioners, policymakers, key administrators, and vendors to stay current with the shifting landscapes within the emerging field of digital pathology. It is also of use to workers in other diagnostic imaging areas such as radiology.

This resource covers various aspects of the use of AI in pathology, including but not limited to the basic principles, advanced applications, challenges in the development, deployment, adoption, and scalability of AI-based models in pathology, the innumerous benefits of applying and integrating AI in the practice of pathology, ethical considerations for the safe adoption and deployment of AI in pathology.

Key features

• Discusses the evolution of machine learning in the field to provide a foundational background

• Addresses challenges in the development, deployment and regulation of AI in anatomic pathology

• Includes information on generative deep learning in digital pathology workflows

• Provides current tools and future perspectives

Readership

Useful for academicians, pathology researchers, practitioners, clinicians, clinical diagnostics researchers, administrators, policymakers, and vendors in the digital pathology field, Higher leadership and administrators of academic and clinical practice centers

Table of contents

PART I PRINCIPLES

1. The evolution of machine learning

2. Basics of machine learning strategies

3. Overview of advanced neural network architectures

4. Complexity in the use of AI in anatomic pathology

5. Quantum Artificial Intelligence: Things to come

6. Dealing with data: strategies for pre-processing

7. Easing the Burden of Annotation in pathology

8. Digital path as a platform for primary diagnosis and augmentation via a deep learning

9. Challenges in the Development, Deployment, and Regulation of AI in Anatomic Pathology

10. Ethics of AI in Pathology: Current Paradigms and Emerging Issues

PART II APPLICATIONS

11. Image enhancement via AI

12. Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images

13. Precision medicine in digital pathology

14. Generative Deep Learning in Digital Pathology Workflows

15. Predictive image-based grading of human cancer

16. The interplay between tumor and immunity

17. Machine-based evaluation intra-tumoral heterogeneity and tumor-stromal interface

PART III OVERVIEW

18. The computer as digital pathology assistant

19. Neuromorphic computing, general AI, and the future of pathology

About the editors

Stanley Cohen

Dr. Cohen is currently interested in integrating computational imaging with digital workflows. He previously served as President of the American Society for Investigative Pathology (ASIP) and Treasurer and Member of the Executive Board of FASEB. Science-related activities also include chairmanships of study sections for the NIH and DOD and membership on multiple editorial boards. He is currently the Associate Editor for digital and computational pathology and artificial intelligence topic category for the American Journal of Pathology. He is a Senior Fellow of the Association of Pathology Chairs and Co-Chair of the ASIP Special Interest Group on Digital and Computational Pathology. Awards include the Gold-Headed Cane (ASIP) and the Golden Goose Award (AAAS). He is a member of the Digital Pathology Association (DPA), the Board of the International Academy of Digital Pathology (IADP), and Chair of the External Advisory Board of the Alpert Foundation.

Chhavi Chauhan

Dr. Chhavi Chauhan works as Director for Scientific Outreach at the American Society for Investigative Pathology and Director of the Continuing Medical Education (CME) Program at the Journal of Molecular Diagnostics. She is one of the leaders of the Women in AI Ethics Collective and an expert at the AI Policy Exchange.  She is a biomedical researcher, expert scholarly communicator, and a sought-after mentor in the fields of scientific research, scholarly publishing, and AI Ethics, especially for women and minorities. She was honored to be featured in The AI Makers 150: top 150 AI &Analytics Leaders & Influencers 2021 list. She is a thought leader, a renowned international speaker, and a strong advocate for equitable and accessible healthcare.  She sits at the intersection of scientific research, scholarly communications, and AI Ethics in Healthcare.   Her vision is to provide equitable personalized healthcare to all, beyond geographies, and despite socioeconomic barriers.