Method of generating training data and medical image processing apparatus for deep-learning-based mammography denoising

Abstract

Systems and methods are provided for generating training data for training a neural network. The neural network may be a machine learning model. The neural network may be a U-Net neural network. Training data for training a neural network may be generated by combining X-ray image data and non-X-ray image data. Training data for training a neural network may be generated by superimposing data having a characteristic related to a lesion onto X-ray image data. The superimposed data may be used to simulate calcification features in mammographic images. Processing circuitry is configured to perform noise reduction by inputting an input image of a breast into a trained machine-learning model to output an output image in which noise included in the input image is reduced in the output image while leaving a characteristic of a lesion included in the input image undeleted in the output image.