Abstract: A method for training a medical image performed by a medical image processing apparatus for processing a medical image for a body is provided. The method for training a medical image includes preparing a first input data set including a training chest X-ray image, and a bone enhancement image or a bone extraction image acquired from the training chest X-ray image; preparing a label data for the first input data set including osteoporosis information or bone mineral density information corresponding to the training chest X-ray image; and training an artificial neural network model using the first input data set and the label data.

Abstract: A method for training a medical image performed by a medical image processing apparatus for processing a medical image for a body is provided. The method for training a medical image includes preparing a first input data set including a training chest X-ray image, and a bone enhancement image or a bone extraction image acquired from the training chest X-ray image; preparing a label data for the first input data set including osteoporosis information or bone mineral density information corresponding to the training chest X-ray image; and training an artificial neural network model using the first input data set and the label data.

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 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: Provided are a method and an apparatus for providing a cloud-based medical collaboration service for medical images. Even if a plurality of users are not in the same space at the same time, since they can provide their opinions at desired time and place through data storing and sharing, remote collaboration is possible without restrictions for physical time and place. Moreover, an AI solution-based artificial intelligence model makes remote collaboration among a plurality of medical experts possible.

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.