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In Neuroimaging clinics of North America

The 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.

Maleki Farhad, Ovens Katie, Najafian Keyhan, Forghani Behzad, Reinhold Caroline, Forghani Reza


Classification, Clustering, Dimensionality reduction, Machine learning, Regression, Supervised learning, Unsupervised learning, Visualization