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Neuroimaging Clin. N. Am. · Nov 2020
ReviewOverview of Machine Learning Part 1: Fundamentals and Classic Approaches.
- Farhad Maleki, Katie Ovens, Keyhan Najafian, Behzad Forghani, Caroline Reinhold, and Reza Forghani.
- Augmented Intelligence & Precision Health Laboratory (AIPHL), Department of Radiology and Research Institute of the McGill University Health Centre, 5252 Boulevard de Maisonneuve Ouest, Montreal, Quebec H4A 3S5, Canada.
- Neuroimaging Clin. N. Am. 2020 Nov 1; 30 (4): e17-e32.
AbstractThe extensive body of research and advances in machine learning (ML) and the availability of a large volume of patient data make ML a powerful tool for producing models with the potential for widespread deployment in clinical settings. This article provides an overview of the classic supervised and unsupervised ML methods as well as fundamental concepts required for understanding how to develop generalizable and high-performing ML applications. It also describes the important steps for developing a ML model and how decisions made in these steps affect model performance and ability to generalize.Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.
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