Fig. 2

Schematic overview of different machine learning components and their interaction for a brain tumor detection example. A model, defined up to some parameters, receives a brain MRI as input and needs to provide as output whether the brain scan shows a tumor or not. Based on example data, i.e., labeled brain MRI, a learning algorithm optimizes the model parameters to improve a certain performance measure. When training is finished and the model achieves sufficient performance, it can be used to detect tumors in new MRI scans