Machine Learning Principles for Radiology Investigators

Artificial intelligence and deep learning are areas of high interest for radiology investigators at present. However, the field of machine learning encompasses multiple statistics-based techniques useful for investigators, which may be complementary to deep learning approaches. After a refresher in basic statistical concepts, relevant considerations for machine learning practitioners are reviewed: regression, classification, decision boundaries, and bias-variance tradeoff. Regularization, ground truth, and populations are discussed along with compute and data management principles. Advanced statistical machine learning techniques including bootstrapping, bagging, boosting, decision trees, random forest, XGboost, and support vector machines are reviewed along with relevant examples from the radiology literature.