Abstract: The invention provides a radiology report editing method and system.

Abstract: The invention provides a model training method and system that uses pretrained features of a teacher neural network trained on a billion-size dataset to train a student neural network. The model training method leverages the teacher neural network to design a more stable loss function that works well with more sophisticated learning rate schedules to reduce training time and make the augmentation designing process more natural.

Abstract: A labeling apparatus according to an embodiment of the present invention includes a pathological data receiving unit configured to receive pathological data about a patient when a first label is a positive index or an uncertain index in a medical image of the patient which is labeled with the first label, a label input unit configured to receive a second label corresponding to the pathological data, a medical image receiving unit configured to receive a medical image corresponding to the pathological data, and a first processing unit configured to label the received medical image with the positive index and store the medical image labeled with the positive index in a positive data set when the second label is the positive index, and label the received medical image with a negative index and store the medical image labeled with the negative index in a negative data set when the second label is the negative index.