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    METHOD AND SYSTEM FOR PROVIDING INTERPRETATION INFORMATION ON PATHOMICS DATA
    101.
    发明申请
    METHOD AND SYSTEM FOR PROVIDING INTERPRETATION INFORMATION ON PATHOMICS DATA 有权

    公开(公告)号：US20210183524A1

    公开(公告)日：2021-06-17

    申请号：US16832142

    申请日：2020-03-27

    Applicant: Lunit, Inc.

    Inventor： Jeong Hoon LEE

    IPC: G16H50/70 ,  G16B50/10 ,  G16H10/40 ,  G16H30/20 ,  G16H30/40 ,  G16H70/60

    Abstract: An operation method of a computing device operated by at least one processor is provided. The operation method comprises receiving pathomics data samples analyzed from slide images of patients and gene samples of the patients, generating a plurality of gene modules by grouping genetic information included in the gene samples, annotating information of databases significantly enriched in each of the gene modules, to a corresponding gene module, based on one-to-one correlation values between the plurality of the gene modules and a plurality of individual pathomics data representing the pathomics data samples, extracting connectivity between the plurality of the individual pathomics data and the plurality of gene modules, and connecting information annotated to each gene module and the individual pathomics data connected to the corresponding gene module.

    METHOD AND SYSTEM FOR DETECTING PNEUMOTHORAX
    102.
    发明申请
    METHOD AND SYSTEM FOR DETECTING PNEUMOTHORAX 有权

    公开(公告)号：US20210059627A1

    公开(公告)日：2021-03-04

    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 ,  G06T7/00 ,  G06T7/70 ,  G06T7/11 ,  G06K9/62 ,  A61M1/04 ,  A61B34/10 ,  G16H50/20 ,  G06N20/00

    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.

    METHOD FOR DISCRIMINATING SUSPICIOUS LESION IN MEDICAL IMAGE, METHOD FOR INTERPRETING MEDICAL IMAGE, AND COMPUTING DEVICE IMPLEMENTING THE METHODS
    103.
    发明申请
    METHOD FOR DISCRIMINATING SUSPICIOUS LESION IN MEDICAL IMAGE, METHOD FOR INTERPRETING MEDICAL IMAGE, AND COMPUTING DEVICE IMPLEMENTING THE METHODS 审中-公开

    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

    IPC: G06T7/00 ,  G06N3/08 ,  G06K9/46

    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.

    Object recognition method and apparatus based on weakly supervised learning
    104.
    发明授权
    Object recognition method and apparatus based on weakly supervised learning 有权

    公开(公告)号：US10102444B2

    公开(公告)日：2018-10-16

    申请号：US15378039

    申请日：2016-12-14

    Applicant: Lunit Inc.

    Inventor： Hyo Eun Kim ,  Sang Heum Hwang

    IPC: G06K9/46 ,  G16H50/20 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00

    Abstract: Provided are an object recognition method and apparatus which determine an object of interest included in a recognition target image using a trained machine learning model and determine an area in which the object of interest is located in the recognition target image. The object recognition method based on weakly supervised learning, performed by an object recognition apparatus, includes extracting a plurality of feature maps from a training target image given classification results of objects of interest, generating an activation map for each of the objects of interest by accumulating the feature maps, calculating a representative value of each of the objects of interest by aggregating activation values included in a corresponding activation map, determining an error by comparing classification results determined using the representative value of each of the objects of interest with the given classification results and updating a CNN-based object recognition model by back-propagating the error.

    MACHINE LEARNING METHOD AND APPARATUS BASED ON WEAKLY SUPERVISED LEARNING
    105.
    发明申请
    MACHINE LEARNING METHOD AND APPARATUS BASED ON WEAKLY SUPERVISED LEARNING 审中-公开

    公开(公告)号：US20180060722A1

    公开(公告)日：2018-03-01

    申请号：US15378001

    申请日：2016-12-13

    Applicant: Lunit Inc.

    Inventor： Sang Heum HWANG ,  Hyo Eun KIM

    IPC: G06N3/04 ,  G06N3/08

    CPC classification number: G06N3/0454 ,  G06N3/084

    Abstract: Provided are a machine learning method based on weakly supervised learning includes extracting feature maps about a dataset given a first type of information and not given a second type of information by using a convolutional neural network (CNN), updating the CNN by back-propagating a first error value calculated as a result of performing a task corresponding to the first type of information by using a first model, and updating the CNN by back-propagating a second error value calculated as a result of performing the task corresponding to the first type of information by using a second model different from the first model, wherein the second type of information is extracted when the task corresponding to the first type of information is performed using the second model.

    Method and system for analyzing pathology image
    106.
    发明授权
    Method and system for analyzing pathology image 有权

    公开(公告)号：US12266196B2

    公开(公告)日：2025-04-01

    申请号：US18491314

    申请日：2023-10-20

    Applicant: Lunit Inc.

    Inventor： Biagio Brattoli ,  Chan-Young Ock ,  Wonkyung Jung ,  Soo Ick Cho ,  Kyunghyun Paeng ,  Dong Geun Yoo

    IPC: G06V20/69 ,  G06V10/22 ,  G06V10/98

    Abstract: Provided is a method for analysing a pathology image, which is performed by at least one processor and includes acquiring a pathology image, inputting the acquired pathology image into a machine learning model and acquiring an analysis result for the pathology image from the machine learning model, and outputting the acquired analysis result, in which the machine learning model is a model trained by using a training data set generated based on a first pathology data set associated with a first domain and a second pathology data set associated with a second domain different from the first domain.

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