Artificial Intelligence and Deep Learning in Pathology
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.
|Author Informaiton||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 at the University of Pennsylvania, Sidney Kimmell Medical College - Thomas Jefferson University, Philadelphia, Pennsylvania|
|Table of Content||
Benjamin R. Mitchell
Anil V. Parwani
Tanishq Abraham, Austin Todd, Daniel A. Orringer and Richard Levenson
Peter D. Caie, Neofytos Dimitriou and Ognjen Arandjelovi'c
Gerardo Fernandez, Abishek Sainath Madduri, Bahram Marami, Marcel Prastawa, Richard Scott, Jack Zeineh and Michael Donovan
Joel Haskin Saltz and Rajarsi Gupta
John E. Tomaszewski
|trim||235 x 191 (7 1/2 x 9 1/4)|