Machine learning for medical images analysis - ScienceDirect

• Machine learning and conventional algorithms are not so different from one another. • Hierarchical-structured algorithms are equivalent to decision trees. • Decision trees can be optimized automatically from training data and thus achieve higher accuracy. • The issue of requiring large amounts of training data is a function of the model/algorithm complexity and not a characteristic of learning-based techniques. Machine learning and conventional algorithms are not so different from one another. Hierarchical-structured algorithms are equivalent to decision trees. Decision trees can be optimized automatically from training data and thus achieve higher accuracy. The issue of requiring large amounts of training data is a function of the model/algorithm complexity and not a characteristic of learning-based techniques. This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be t

• Machine learning and conventional algorithms are not so different from one another. • Hierarchical-structured algorithms are equivalent to decision trees. • Decision trees can be optimized automatically from training data and thus achieve higher accuracy. • The issue of requiring large amounts of training data is a function of the model/algorithm complexity and not a characteristic of learning-based techniques. Machine learning and conventional algorithms are not so different from one another. Hierarchical-structured algorithms are equivalent to decision trees. Decision trees can be optimize