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公开(公告)号:US20240071621A1
公开(公告)日:2024-02-29
申请号:US18270895
申请日:2022-02-09
Applicant: Lunit Inc.
Inventor: Ki Hwan KIM , Hyeonseob NAM
CPC classification number: G16H50/30 , G06T7/0012 , G16H30/40 , G16H50/20 , G06T2207/20081 , G06T2207/20084 , G06T2207/30068 , G06T2207/30096
Abstract: A method for predicting a risk of occurrence of a lesion is provided, which is performed by one or more processors and includes acquiring a medical image of a subject, using a machine learning model, predicting a possibility of occurrence of a lesion of the subject from acquired medical image, and outputting a prediction result, in which the machine learning model may be a model trained with a plurality of training medical images and a risk of occurrence of the lesion associated with each training medical image.
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公开(公告)号:US20210073990A1
公开(公告)日:2021-03-11
申请号:US17073065
申请日:2020-10-16
Applicant: Lunit Inc.
Inventor: Nayoung JEONG , Ki Hwan KIM , Minhong JANG
Abstract: Provided are a computerized image interpretation method and a device for analyzing a medical image. The image interpretation method may include receiving, at a processor, a medical image, and receiving report information including a healthcare worker's judgement result of the medical image. The method may also include generating, at the processor, result information representing correspondence between first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, by applying the first lesion information and the second lesion information to a third analysis model. The method may further include outputting, at the processor, the result information.
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公开(公告)号:US20250124577A1
公开(公告)日:2025-04-17
申请号:US18986988
申请日:2024-12-19
Applicant: Lunit Inc.
Inventor: Nayoung JEONG , Ki Hwan KIM , Minhong JANG
Abstract: Provided are a computerized image interpretation method and a device for analyzing a medical image. The image interpretation method may include receiving, at a processor, a medical image, and receiving report information including a healthcare worker's judgement result of the medical image. The method may also include generating, at the processor, result information representing correspondence between first lesion information, which is related to a lesion in the medical image acquired on the basis of the medical image, and second lesion information, which is related to a lesion in the medical image acquired on the basis of the report information, by applying the first lesion information and the second lesion information to a third analysis model. The method may further include outputting, at the processor, the result information.
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