• Artificial Intelligence and Deep Learning in Pathology (Paperback / softback) 9780323675383
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Artificial Intelligence and Deep Learning in Pathology

Gain a clear understanding of powerful new digital tools and their vast potential in pathology

Stanley Cohen (Edited by)

9780323675383, Elsevier Health Sciences

Paperback / softback, published 2 June 2020

288 pages
23.4 x 19 x 1.9 cm, 0.59 kg

"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)

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.

  • 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.

  1. The evolution of machine learning: past, present, and future

    2. Stanley Cohen

  2. The basics of machine learning: strategies and techniques

    3. Stanley Cohen

  3. Overview of advanced neural network architectures

    4. Benjamin R. Mitchell

  4. Complexity in the use of artificial intelligence in anatomic pathology

    5. Stanley Cohen

  5. Dealing with data: strategies of preprocessing data

    6. Stanley Cohen

  6. Digital pathology as a platform for primary diagnosis and augmentation via deep learning.

    7. Anil V. Parwani

  7. Applications of artificial intelligence for image enhancement in pathology

    8. Tanishq Abraham, Austin Todd, Daniel A. Orringer and Richard Levenson

  8. Precision medicine in digital pathology via image analysis and machine learning

    9. Peter D. Caie, Neofytos Dimitriou and Ognjen Arandjelovi'c

  9. Artificial intelligence methods for predictive image-based grading of human cancers

    10. Gerardo Fernandez, Abishek Sainath Madduri, Bahram Marami, Marcel Prastawa, Richard Scott, Jack Zeineh and Michael Donovan

  10. Artificial intelligence and the interplay between tumor and immunity

    11. Joel Haskin Saltz and Rajarsi Gupta

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

John E. Tomaszewski

Subject Areas: Histopathology [MMFH], Pathology [MMF]
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