Machine learning device and method

Abstract

Provided are a machine learning device and a method capable of performing machine learning of labeling for accurately attaching a plurality of labels to volume data at once by using learning data with mixed inconsistent labeling. A neural network () receives an input of multi-slice images of learning data Di (i=1, 2, . . . n) of which a class to be labeled is n types, and creates a prediction mask of n anatomical structures i by a convolutional neural network (CNN) or the like (S). A machine learning unit () calculates a prediction accuracy acc(i) of the class corresponding to the learning data Di for each learning data Di (S). The machine learning unit () calculates a weighted average M of an error di between the prediction accuracy acc(i) and a ground truth mask Gi. The machine learning unit () calculates a learning loss by a loss function Loss (S). The machine learning unit () changes each coupling load of the neural network () from an output layer side to an input layer side according to a value of the learning loss calculated by the loss function Loss (S).