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Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods
Hooman H. Rashidi , MD, FASCP 1 , Nam K. Tran , PhD, HCLD (ABB), FACB 1 , Elham Vali Betts , MD, FASCP 1 , Lydia P. Howell , MD, FASCP, FCAP 1 , Ralph Green , MD, PhD, FASCP, FCAP, FRCPath 1
03 September 2019
algorithms, artificial intelligence, convolutional neural network, deep learning, k-nearest neighbor, machine learning, random forest, supervised learning, supervised methods, support vector machine, unsupervised learning
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Abstract
Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).
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Most cited references 32
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Support vector machines
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Some Studies in Machine Learning Using the Game of Checkers
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10.1177_2374289519873088This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License ( http://www.creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).