Abstract: The present disclosure relates to an apparatus and a method for analyzing medical data based on unsupervised learning. By using a machine learning model based on an adversarial generative neural network to detect and notify anomalies in medical data, the present disclosure allows accurate and rapid reading, in addition to saving time and cost incurred by reading.

Abstract: The present disclosure relates to image learning method, apparatus, program, and recording medium using a generative adversarial network. The present disclosure allows to learn various images as well as medical radiographic images to maintain structural information on the basis of a generative adversarial network. The present disclosure prevents the structural information of the generated image with respect to an original image from being lost, and improves image qualities, such as resolution, noise degree, contrast, etc. to the level of a target reference dataset. When the present disclosure is used for image standardization, medical radiographic images imaged by different institutions and any number of image datasets having various qualities can be standardized universally.

Abstract: The present disclosure relates to an apparatus and a method for analyzing medical data based on unsupervised learning. By using a machine learning model based on an adversarial generative neural network to detect and notify anomalies in medical data, the present disclosure allows accurate and rapid reading, in addition to saving time and cost incurred by reading.

Abstract: A medical image processing method for processing a pediatric simple X-ray image using a machine learning model and a medical image processing apparatus therefor are provided. The apparatus applies a style conversion model to a CT image pair including a basic CT image and a suppression CT image where at least a portion of a bone of the basic CT image is suppressed to convert the CT image pair into a conversion image pair and trains a bone suppression model for a simple X-ray image based on training data including the conversion image pair.

Abstract: The present disclosure relates to image learning method, apparatus, program, and recording medium using a generative adversarial network. The present disclosure allows to learn various images as well as medical radiographic images to maintain structural information on the basis of a generative adversarial network. The present disclosure prevents the structural information of the generated image with respect to an original image from being lost, and improves image qualities, such as resolution, noise degree, contrast, etc. to the level of a target reference dataset. When the present disclosure is used for image standardization, medical radiographic images imaged by different institutions and any number of image datasets having various qualities can be standardized universally.