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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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Patent Agency Ranking
公开(公告)号：US20240193774A1
公开(公告)日：2024-06-13
申请号：US18584049
申请日：2024-02-22
Applicant: Lunit Inc.
Inventor： Hyo-Eun KIM , Hyeonseob NAM
IPC: G06T7/00 , G06N3/08 , G06V10/40 , G06V10/764
CPC classification number: G06T7/0012 , G06N3/08 , G06V10/40 , G06V10/764 , G06T2207/20081 , G06T2207/20084 , G06T2207/30068 , G06T2207/30096
Abstract: A method for interpreting an input image by a computing device operated by at least one processor is provided. The method for interpreting an input image comprises storing an artificial intelligent (AI) model that is trained to classify a lesion detected in the input image as suspicious or non-suspicious and, under a condition of being suspicious, to classify the lesion detected in the input image as malignant or benign-hard representing that the lesion is suspicious but determined to be benign, receiving an analysis target image, by using the AI model, obtaining a classification class of a target lesion detected in the analysis target image and, when the classification class is the suspicious, obtaining at least one of a probability of being suspicious, a probability of being benign-hard, and a probability of malignant for the target lesion, and outputting an interpretation result including at least one probability obtained for the target lesion.
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公开(公告)号：US20210125059A1
公开(公告)日：2021-04-29
申请号：US16842373
申请日：2020-04-07
Applicant: Lunit Inc.
Inventor： HyunJae LEE , Hyeonseob NAM
Abstract: Provided is a method for training a neural network and a device thereof. The method may train a neural network including first and second layers in a computing device. The method may include acquiring, at a processor of the computing device, a layer output of the first layer for training data and extracting, at the processor, statistics information of the layer output. The method may also include normalizing, at the processor, the layer output through the statistics information to generate a normalized output and augmenting, at the processor, the statistics information to generate augmented statistics information associated with the statistics information. The method may further include performing, at the processor, an affine transform on the normalized output using the augmented statistics information to generate a transformed output and providing, at the processor, the transformed output as an input to the second layer.
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公开(公告)号：US20200372641A1
公开(公告)日：2020-11-26
申请号：US16874926
申请日：2020-05-15
Applicant: Lunit Inc.
Inventor： Hyo-Eun KIM , Hyeonseob NAM
Abstract: A method for interpreting an input image by a computing device operated by at least one processor is provided. The method for interpreting an input image comprises storing an artificial intelligent (AI) model that is trained to classify a lesion detected in the input image as suspicious or non-suspicious and, under a condition of being suspicious, to classify the lesion detected in the input image as malignant or benign-hard representing that the lesion is suspicious but determined to be benign, receiving an analysis target image, by using the AI model, obtaining a classification class of a target lesion detected in the analysis target image and, when the classification class is the suspicious, obtaining at least one of a probability of being suspicious, a probability of being benign-hard, and a probability of malignant for the target lesion, and outputting an interpretation result including at least one probability obtained for the target lesion.
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5.发明公开公开(公告)号：US20240233948A9
公开(公告)日：2024-07-11
申请号：US18273316
申请日：2022-03-30
Applicant: Lunit Inc.
Inventor： Hyeonsoo LEE , Kihwan KIM , Hyeonseob NAM
Abstract: A prediction device operated by at least one processor includes: a risk factor inference model implemented with an artificial intelligence model trained to infer risk factors for a disease from input images, configured to receive medical images and output at least one inferred risk factor; and a medical prediction model configured to receive patient information including the at least one inferred risk factor as input and output a medical prediction including a disease risk.
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6.发明公开公开(公告)号：US20240136068A1
公开(公告)日：2024-04-25
申请号：US18273316
申请日：2022-03-29
Applicant: Lunit Inc.
Inventor： Hyeonsoo LEE , Kihwan KIM , Hyeonseob NAM
Abstract: A prediction device operated by at least one processor includes: a risk factor inference model implemented with an artificial intelligence model trained to infer risk factors for a disease from input images, configured to receive medical images and output at least one inferred risk factor; and a medical prediction model configured to receive patient information including the at least one inferred risk factor as input and output a medical prediction including a disease risk.
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