公开(公告)号：US20240029258A1

公开(公告)日：2024-01-25

申请号：US18270886

申请日：2022-02-08

Applicant: LUNIT INC.

Inventor： Minchul KIM ,  Gunhee NAM ,  Thijs KOOI

IPC: G06T7/00 ,  G06T7/62

CPC classification number: G06T7/0016 ,  G06T7/62 ,  G06T2207/10116 ,  G06T2207/30096 ,  G06T2207/20081 ,  G06T2207/30061 ,  G06T2207/20084

Abstract: A method for measuring a size change of a target lesion in an X-ray image is provided, including receiving a first X-ray image including the target lesion and a second X-ray image including the target lesion, calculating an occupancy of a region corresponding to the target lesion in criterion regions in each of the first X-ray image and the second X-ray image, and measuring a size change of the target lesion based on the calculated occupancies.

公开(公告)号：US11844632B2

公开(公告)日：2023-12-19

申请号：US18177266

申请日：2023-03-02

Applicant: LUNIT INC.

Inventor： Donggeun Yoo ,  Sanghyup Lee ,  Minchul Kim ,  Hanjun Lee ,  Sunggyun Park

IPC: G06T7/00 ,  A61B6/00 ,  A61B5/00

CPC classification number: A61B5/7264 ,  A61B6/5217 ,  G06T7/0012

Abstract: A method for determining an abnormality in a medical device from a medical image is provided. The method for determining an abnormality in a medical device comprises receiving a medical image, and detecting information on at least a part of a target medical device included in the received medical image.

公开(公告)号：US11823056B2

公开(公告)日：2023-11-21

申请号：US16842435

申请日：2020-04-07

Applicant: Lunit Inc.

Inventor： HyunJae Lee ,  Hyo-Eun Kim ,  Weonsuk Lee

IPC: G06N3/084 ,  G06N20/20 ,  G06T5/50 ,  G06V10/82 ,  G06N3/045 ,  G06V10/774 ,  G06V10/44 ,  G06V20/64

CPC classification number: G06N3/084 ,  G06N3/045 ,  G06N20/20 ,  G06T5/50 ,  G06V10/44 ,  G06V10/7747 ,  G06V10/82 ,  G06V20/647 ,  G06T2207/10081 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30068

Abstract: Provided is a method for training a neural network and a device thereof. The method may train a neural network with three-dimensional (3D) training image data including a plurality of two-dimensional (2D) training image data. The method may include training, at a processor, a first convolutional neural network (CNN) with the plurality of 2D training image data, wherein the first convolutional neural network comprises 2D convolutional layers. The method may further include training, at the processor, a second convolutional neural network with the 3D training image data, wherein the second convolutional neural network comprises the 2D convolutional layers and 3D convolutional layers configured to receive an output of the 2D convolutional layers as an input.

公开(公告)号：US20230298171A1

公开(公告)日：2023-09-21

申请号：US18122837

申请日：2023-03-17

Applicant: LUNIT INC.

Inventor： Jeong Seok KANG ,  Dong Geun YOO ,  Soo Ick CHO ,  Won Kyung JUNG

IPC: G06T7/00 ,  G06V10/74 ,  G06F3/14 ,  G16H15/00 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06V10/761 ,  G06F3/14 ,  G16H15/00 ,  G16H50/20 ,  G06T2207/20081 ,  G06T2207/30024

Abstract: A computing device includes at least one memory, and at least one processor configured to generate, based on first analysis on a pathological slide image, first biomarker expression information, generate, based on a user input for updating at least some of results of the first analysis, second biomarker expression information about the pathological slide image, and control a display device to output a report including medical information about at least some regions included in the pathological slide image, based on at least one of the first biomarker expression information or the second biomarker expression information.

公开(公告)号：US20230030313A1

公开(公告)日：2023-02-02

申请号：US17858330

申请日：2022-07-06

Applicant: LUNIT INC.

Inventor： Jong Seok AHN ,  Jeong Hoon LEE

IPC: G16H50/20 ,  G16H30/20

Abstract: Provided is a method, performed by at least one computing apparatus, of generating an interpretable prediction result for a patient. The method includes receiving medical image data of a subject patient, receiving additional medical data of the subject patient, and generating information about a prediction result for the subject patient, based on the medical image data of the subject patient and the additional medical data of the subject patient, by using a machine learning prediction model.

公开(公告)号：US20220092448A1

公开(公告)日：2022-03-24

申请号：US17383937

申请日：2021-07-23

Applicant: LUNIT INC.

Inventor： In Wan YOO ,  Donggeun YOO

IPC: G06N5/04 ,  G06N20/00

Abstract: Provided is a method for training a hint-based machine learning model configured to infer annotation information for target data, including obtaining training data for the machine learning model, wherein the training data includes a plurality of target data items provided with a plurality of annotation information items, and extracting a plurality of pixel groups from the plurality of target data items. The extracted plurality of pixel groups may be included in hint information. In addition, the method includes obtaining, from the plurality of annotation information items, a plurality of annotation classes corresponding to the extracted plurality of pixel groups to include the obtained plurality of annotation classes in the hint information, and training, by using the hint information, the machine learning model to infer the plurality of annotation information items associated with the plurality of target data items.

公开(公告)号：US20220036971A1

公开(公告)日：2022-02-03

申请号：US17502339

申请日：2021-10-15

Applicant: LUNIT INC.

Inventor： Donggeun YOO ,  Chanyoung OCK ,  Kyunghyun PAENG

IPC: G16B20/00 ,  G06N3/02

Abstract: The present disclosure relates to a method, performed by at least one computing device, for predicting a response to an immune checkpoint inhibitor. The method includes receiving a first pathology slide image, detecting one or more target items in the first pathology slide image, determining at least one of an immune phenotype of at least some regions in the first pathology slide image or information associated with the immune phenotype based on the detection result for the one or more target items, and generating a prediction result as to whether or not a patient associated with the first pathology slide image responds to the immune checkpoint inhibitor, based on the immune phenotype of the at least some regions in the first pathology slide image or the information associated with the immune phenotype.

公开(公告)号：US20210357694A1

公开(公告)日：2021-11-18

申请号：US17097036

申请日：2020-11-13

Applicant: Lunit Inc.

Inventor： HyunJae LEE

IPC: G06K9/62 ,  G06K9/46 ,  G06K9/00 ,  G16H30/40 ,  G06N3/08

Abstract: A 3D image sliced into a plurality of slices including the first slice on which a label is annotated and a plurality of second slices on which the label is not annotated is provided as a training sample. A computing device trains a neural network based on the first slice, determines an expandable second slice which is expandable from the first slice from among the plurality of second slices based on the trained neural network; and trains the neural network based on expanded slices including the expandable second slice.

公开(公告)号：US20210271938A1

公开(公告)日：2021-09-02

申请号：US17320424

申请日：2021-05-14

Applicant: Lunit Inc.

Inventor： Jae Hwan Lee

IPC: G06K9/62 ,  G06K9/66 ,  G06N3/08

Abstract: A normalization method for machine learning and an apparatus thereof are provided. The normalization method according to some embodiments of the present disclosure may calculate a value of a normalization parameter for an input image through a normalization model before inputting the input image to a target model and normalize the input image using the calculated value of the normalization parameter. Because the normalization model is updated based on a prediction loss of the target model, the input image can be normalized to an image suitable for a target task, so that stability of the learning and performance of the target model can be improved.

公开(公告)号：US11100359B2

公开(公告)日：2021-08-24

申请号：US16694826

申请日：2019-11-25

Applicant: Lunit Inc.

Inventor： Minje Jang

IPC: G06K9/46 ,  G06K9/00 ,  G06K9/62 ,  G06T7/00

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.