Machine learning device and method

Abstract

Provided is a machine learning device and method that enables machine learning of labeling, in which a plurality of labels are attached to volume data at one effort with excellent accuracy, using training data having label attachment mixed therein. A probability calculation unit () calculates a value (soft label) indicating a likelihood of labeling of a class Ci for each voxel of a second slice image by means of a learned teacher model (). A detection unit () detects 'bronchus' and “blood vessel” for the voxels of the second slice image using a known method, such as a region expansion method and performs labeling of “bronchus” and “blood vessel”. A correction probability setting unit () replaces the soft label with a hard label of “bronchus” or “blood vessel” detected by the detection unit (). A distillation unit () performs distillation of a student model () from the teacher model () using the soft label after correction by means of the correction probability setting unit (). With this, the learned student model () is obtained.