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公开(公告)号：US11574727B2
公开(公告)日：2023-02-07
申请号：US17077142
申请日：2020-10-22
Applicant: Lunit Inc.
Inventor： Jongchan Park
Abstract: A method of reading a medical image by a computing device operated by at least one processor is provided. The method includes obtaining an abnormality score of the input image using an abnormality prediction model, filtering the input image so as not to be subsequently analyzed when the abnormality score is less than or equal to a cut-off score based on the cut-off score which makes a specific reading sensitivity; and obtaining an analysis result of the input image using a classification model that distinguishes the input image into classification classes when the abnormality score is greater than the cut-off score.
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公开(公告)号：US11564650B2
公开(公告)日：2023-01-31
申请号：US16680783
申请日：2019-11-12
Applicant: Lunit Inc. , SEOUL NATIONAL UNIVERSITY HOSPITAL
Inventor： Min Chul Kim , Chang Min Park , Eui Jin Hwang
IPC: A61B6/00 , A61B34/10 , G06T7/11 , G06T7/70 , G16H50/20 , G06N20/00 , A61M1/04 , G06K9/62 , G06T7/00
Abstract: Some embodiments of the present disclosure provide a pneumothorax detection method performed by a computing device. The method may comprise obtaining predicted pneumothorax information, predicted tube information, and a predicted spinal baseline with respect to an input image from a trained pneumothorax prediction model; determining at least one pneumothorax representative position for the predicted pneumothorax information and at least one tube representative position for the predicted tube information, in a prediction image in which the predicted pneumothorax information and the predicted tube information are displayed; dividing the prediction image into a first region and a second region by the predicted spinal baseline; and determining a region in which the at least one pneumothorax representative position and the at least one tube representative position exist among the first region and the second region.
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公开(公告)号：US20220269905A1
公开(公告)日：2022-08-25
申请号：US17685740
申请日：2022-03-03
Applicant: Lunit Inc.
Inventor： Jongchan PARK , Donggeun YOO
Abstract: 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.
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公开(公告)号：US20220172009A1
公开(公告)日：2022-06-02
申请号：US17671936
申请日：2022-02-15
Applicant: LUNIT INC.
Inventor： In Wan YOO
Abstract: Provided is a method for performing a prediction work on a target image, including dividing the target image into a plurality of sub-images, generating prediction results for a plurality of pixels included in each of the plurality of divided sub-images, applying weights to the prediction results for the plurality of pixels, and merging the prediction results for the plurality of pixels applied with the weights.
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公开(公告)号：US20220101984A1
公开(公告)日：2022-03-31
申请号：US17426336
申请日：2020-05-22
Applicant: Lunit Inc.
Inventor： Jong Chan PARK , Dong Geun YOO , Ki Hyun YOU , Hyeon Seob NAM , Hyun Jae LEE , Sang Hyup LEE
IPC: G16H30/40 , G16H30/20 , G06V30/166 , G06N20/00
Abstract: The present disclosure relates to a medical image analysis method using a processor and a memory which are hardware. The method includes generating predicted second metadata for a medical image by using a prediction model, and determining a processing method of the medical image based on one of first metadata stored corresponding to the medical image and the second metadata.
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Patent Agency Ranking
公开(公告)号：US11270203B2
公开(公告)日：2022-03-08
申请号：US16438776
申请日：2019-06-12
Applicant: Lunit Inc.
Inventor： Hyeon Seob Nam , Hyo Eun Kim
IPC: G06N3/08
Abstract: There is provided is a method and an apparatus for training a neural network capable of improving the performance of the neural network by performing intelligent normalization according to a target task of the neural network. The method according to some embodiments of the present disclosure includes transforming the output data into first normalized data using a first normalization technique, transforming the output data into second normalized data using a second normalization technique and generating target normalized data by aggregating the first normalized data and the second normalized data based on a learnable parameter. At this time, a rate at which the first normalization data is applied in the target normalization data is adjusted by the learnable parameter so that the intelligent normalization according to the target task can be performed, and the performance of the neural network can be improved.
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公开(公告)号：US20210342627A1
公开(公告)日：2021-11-04
申请号：US17375876
申请日：2021-07-14
Applicant: Lunit Inc.
Inventor： Minje JANG
Abstract: An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.
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公开(公告)号：US20210103757A1
公开(公告)日：2021-04-08
申请号：US16694826
申请日：2019-11-25
Applicant: Lunit Inc.
Inventor： Minje JANG
Abstract: An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.
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公开(公告)号：US20200372362A1
公开(公告)日：2020-11-26
申请号：US16706570
申请日：2019-12-06
Applicant: Lunit Inc.
Inventor： Hyo-Eun KIM
Abstract: This disclosure relates to a method of sequential machine learning of data sets and an apparatus thereof. The method may include generating a first machine learning model by generating a first feature space based on a first data set, generating first predictive label information based on the first feature space, performing machine learning on a relationship between the first data set and first label information related to a first data set, and performing machine learning on a relationship between the first predictive label information and the first feature space. The method may also include generating a second machine learning model based on the first machine learning model by generating a second feature space based on a second data set, generating second predictive label information based on the second feature space, and performing machine learning on a relationship between the second data set and a second label information.
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公开(公告)号：US10824944B2
公开(公告)日：2020-11-03
申请号：US16676694
申请日：2019-11-07
Applicant: Lunit Inc.
Inventor： Hyun Jae Lee
Abstract: A method of recalibrating a feature data of each channel generated by a convolution layer of a convolution neural network is provided. According to some embodiments, since an affine transformation is applied to the feature data of each channel independently of the feature data of the other channel, the overall number of parameters defining the affine transformation is minimized. As a result, the amount of computations required in performing the feature data recalibration can be reduced.
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