Artificial Intelligence and Deep Learning in Pathology

Book Artificial Intelligence and Deep Learning in Pathology Cover

Download or read online Artificial Intelligence and Deep Learning in Pathology PDF book by Stanley Cohen in ePUB, PDF or Kindle eBooks. Published by Elsevier Health Sciences in 02 June 2020 the book become immediate popular and critical acclaim in Medical books with total hardcover page 288. Click Download Book button to get book file. Read some info about this book below.

  • Author : Stanley Cohen
  • Publisher : Elsevier Health Sciences
  • Release : 02 June 2020
  • ISBN : 9780323675376
  • Page : 288 pages
  • Language : English
  • Total View : 8862 Views
  • File Size : 40,8 Mb

Artificial Intelligence and Deep Learning in Pathology Book PDF summary

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. 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.

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