Provided are machine learning device and method which are capable of machine-learning labeling for accurately attaching, to volume data, a plurality of labels at once by using learning data in which labelings are intermingled. A neural network (14) receives an input of multiple slice images of learning data Di (i=1, 2, …n) in which classes to be labeled are n types, and constructs (S1) a prediction mask of n anatomical structures i by means of a convolutional neural network (CNN) or the like. A machine learning unit (13) calculates (S2), for each piece of the learning data Di, prediction accuracy acc(i) of the class corresponding to the learning data Di. The machine learning unit (13) calculates a weighted average M of an error di between the prediction accuracy acc(i) and a correct answer mask Gi. The machine learning unit (13) calculates (S4) a learning loss with a loss function Loss. The machine learning unit (13) changes (S5), according to the learning loss value calculated with the loss function Loss, each connection weight of the neural network (14) from an output layer side to an input layer side.
MACHINE LEARNING DEVICE AND METHOD