Supervised learning (machine learning)
Supervised learning is the most common type of machine learning algorithm used in medical imaging research. It involves training an algorithm from a set of images or data where the output labels are already known ^{1}.
Supervised learning is broken into two subcategories, classification and regression ^{2}. Classification refers to the prediction of whether an image falls into one or more categories while regression aims to predict a continuous label. Medical imaging machine learning deals mainly with the classification of images.
Fundamentally, the optimization of supervised machine learning consists of two phases that are looped through continuously ^{3}:
 forward propagation: for a given input, calculates a predicted outcome and compares this with the expected outcome to give an overall cost (error)
 backward propagation: from the cost, works backwards through the network updating the parameters in an attempt to minimize the overall cost
Radiology example
 simplified, a given input, (such as a lung nodule ROI) calculates the predicted outcome over the expected outcome once the cost is estimated the network will update the individual weighting of the output parameters (think of it as a vote)
 the output parameters that correctly identified the nodule to the expected outcome are awarded a heavier 'vote'
 the output parameters that did not correctly identified the nodule to the expected outcome are adjusted to minimize cost
Related Radiopaedia articles
Artificial intelligence
 artificial intelligence (AI)
 imaging data sets
 computeraided diagnosis (CAD)
 machine learning (overview)
 models
 common data preparation/preprocessing steps
 DICOM to bitmap conversion
 scaling
 centering
 normalization
 principal component analysis
 training, testing and validation datasets
 augmentation

loss function
 mean squared error
 cross entropy
 optimization algorithms
 stochastic gradient descent
 momentum (Nesterov)
 ADAM

regularisation
 linear and quadratic
 batch normalization

ensembling
 boosting
 bagging
 natural language processing
 rulebased expert systems
 glossary