LUNIT INC.

A method for generating a medical prediction related to a biomarker from medical data is provided, which includes obtaining medical data associated with a patient, determining a region of interest in the medical data, extracting one or more features associated with the medical data based on the region of interest, and generating a medical prediction for the patient based on the extracted one or more features.

There is provided a method and apparatus that collects feature points of data and performs machine learning. A machine learning method comprises receiving first feature data obtained by applying a basic model to first analysis target data, receiving second feature data obtained by applying the basic model to second analysis target data, and obtaining a final machine learning model through performing machine learning on a correlation between the first feature data and first analysis result data and a correlation between the second feature data and second analysis result data.

A machine learning method that may reduce an annotation cost and may improve performance of a target model is provided. Some embodiments of the present disclosure may provide a machine learning method performed by a computing device, including: acquiring a training dataset of a first model including a plurality of data samples to which label information is not given; calculating a miss-prediction probability of the first model on the plurality of data samples; configuring a first data sample group by selecting at least one data sample from the plurality of data samples based on the calculated miss-prediction probability; acquiring first label information on the first data sample group; and performing first learning on the first model by using the first data sample group and the first label information.

This disclosure relates to a computerized method to perform a machine learning on a relationship between medical images and metadata using a neural network and acquiring metadata by applying a machine learning model to medical images, and a method thereof. The apparatus and method may include training a prediction model for predicting metadata of medical images based on multiple medical images for learning and metadata matched with each of multiple medical images and predicting metadata of input medical image.