Artificial Intelligence and Deep Learning in Pathology

Edited by Stanley Cohen, MD
Publication Date
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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, with a team of experts, 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.

Key Features
  • 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.
ISBN 9780323675383
Product Format Book
Publication Date 2020
Author Information Edited by Stanley Cohen, MD, Emeritus Founding Director, Center for Biophysical Pathology, Rutgers-NJMS; Adjunct Professor of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Perelman School of Medicine, University of Pennsylvania, Sidney Kimmell Medical College - Thomas Jefferson University, Philadelphia, Pennsylvania, USA
Published Reviews

"We do, however, need to understand AI and adapt to it. This book is a great introduction, as well as a stimulating read. It is recommended for those interested in AI, software or the future of pathology." -Dr Niall O'Neill (Bulletin of the Royal College of Pathologists, January 2021)

Table of Content
  1. The evolution of machine learning: past, present, and future
  2. Stanley Cohen

  3. The basics of machine learning: strategies and techniques
  4. Stanley Cohen

  5. Overview of advanced neural network architectures
  6. Benjamin R. Mitchell

  7. Complexity in the use of artificial intelligence in anatomic pathology
  8. Stanley Cohen

  9. Dealing with data: strategies of preprocessing data
  10. Stanley Cohen

  11. Digital pathology as a platform for primary diagnosis and augmentation via deep learning.
  12. Anil V. Parwani

  13. Applications of artificial intelligence for image enhancement in pathology
  14. Tanishq Abraham, Austin Todd, Daniel A. Orringer and Richard Levenson

  15. Precision medicine in digital pathology via image analysis and machine learning
  16. Peter D. Caie, Neofytos Dimitriou and Ognjen Arandjelovi'c

  17. Artificial intelligence methods for predictive image-based grading of human cancers
  18. Gerardo Fernandez, Abishek Sainath Madduri, Bahram Marami, Marcel Prastawa, Richard Scott, Jack Zeineh and Michael Donovan

  19. Artificial intelligence and the interplay between tumor and immunity
  20. Joel Haskin Saltz and Rajarsi Gupta

  21. Overview of the role of artificial intelligence in pathology: the computer as a pathology digital assistant

John E. Tomaszewski

Publication Date 02-06-2020
Pages 270
Trim 235 x 191 (7 1/2 x 9 1/4)
Stock Status In Stock
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