Method of generating trained model, machine learning system, program, and medical image processing apparatus

A machine learning system for training a learning model that converts a domain of a medical image which is input, and generates a generated image of a different domain, the system comprising:

• at least one first processor;
• and
• at least one first storage device in which a program executed by the at least one first processor is stored,
• wherein the learning model having a structure of a generative adversarial network including
• a first generator configured using a first convolutional neural network that receives an input of a medical image of a first domain corresponding to a first medical imaging apparatus and that outputs a pseudo image of a second domain different from the first domain, corresponding to a second medical imaging apparatus different from the first medical imaging apparatus, and
• a first discriminator configured using a second convolutional neural network that receives an input of data including first image data and coordinate information of a human body coordinate system corresponding to each position of a plurality of unit elements configuring the first image data, and that discriminates whether an input image related to the second domain is a real image imaged by the second imaging apparatus or a fake image generated by the first generator, wherein the first image data is the pseudo image of the second domain or an actual medical image of the second domain included in a training dataset, and
• the at least one first processor, by executing an instruction of the program,
• acquires a plurality of pieces of training data including a first medical image imaged by the first medical imaging apparatus and a second medical image imaged by the second medical imaging apparatus having a same imaging region, and
• performs training processing of training the first generator and the first discriminator in an adversarial manner based on the plurality of pieces of training data.