公开(公告)号：US20230281971A1

公开(公告)日：2023-09-07

申请号：US18178233

申请日：2023-03-03

Applicant: Lunit Inc.

Inventor： Dong Geun YOO ,  Sang Hoon SONG ,  Chan Young OCK ,  Won Kyung JUNG ,  Soo Ick CHO ,  Kyung Hyun PAENG

IPC: G06V10/774 ,  G06V20/69 ,  G06T7/00

CPC classification number: G06V10/774 ,  G06V20/698 ,  G06T7/0012 ,  G06V2201/03 ,  G06T2207/30024 ,  G06T2207/20081

Abstract: Provided is a computing device including at least one memory, and at least one processor configured to obtain a first pathological slide image one of a first object and biological information of the first object, generate training data by using at least one first patch included in the first pathological slide image, and the biological information, train a first machine learning model based on the training data, and analyze a second pathological slide image of a second object by using the trained first machine learning model.

公开(公告)号：US20240249826A1

公开(公告)日：2024-07-25

申请号：US18416285

申请日：2024-01-18

Applicant: Lunit Inc.

Inventor： Kyung Hyun PAENG ,  Chan Young OCK ,  Dong Geun YOO

IPC: G16H30/40 ,  G16H20/10

CPC classification number: G16H30/40 ,  G16H20/10

Abstract: Provided is a computing device including at least one memory, and at least one processor configured to obtain feature information corresponding to a pathological slide image, generate medical information associated with the pathological slide image based on the feature information, and output at least one of the medical information and additional information based on the medical information.

公开(公告)号：US20230206432A1

公开(公告)日：2023-06-29

申请号：US17703484

申请日：2022-03-24

Applicant: LUNIT INC.

Inventor： Ga Hee PARK ,  Chan Young OCK ,  Kyung Hyun PAENG

IPC: G06T7/00 ,  G16H30/20 ,  G16H30/40

CPC classification number: G06T7/0012 ,  G16H30/20 ,  G16H30/40 ,  G06T2207/30096 ,  G16H50/20

Abstract: Provided is a computing apparatus including: at least one memory; and at least one processor, wherein the at least one processor is configured to: perform a first classification on a plurality of tissues expressed in a pathological slide image by analyzing the pathological slide image, perform a second classification on a plurality of cells expressed in a pathological slide image by analyzing the pathological slide image, and calculate tumor purity including information on noise included in the pathological slide image by combining a first classification result and a second classification result.

公开(公告)号：US20250132021A1

公开(公告)日：2025-04-24

申请号：US18989313

申请日：2024-12-20

Applicant: Lunit Inc.

Inventor： Kyoung Won LEE ,  Kyung Hyun PAENG

IPC: G16H30/40 ,  G06F18/21 ,  G06F40/169 ,  G06N3/08 ,  G06Q10/0631 ,  G06Q10/0639 ,  G06T7/00 ,  G06V10/22 ,  G06V10/774 ,  G06V10/776 ,  G06V10/778 ,  G16H10/40 ,  G16H30/20 ,  G16H40/20

Abstract: A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.

公开(公告)号：US20230335259A1

公开(公告)日：2023-10-19

申请号：US17720549

申请日：2022-04-14

Applicant: Lunit Inc.

Inventor： Kyoung Won LEE ,  Kyung Hyun PAENG

IPC: G16H30/40 ,  G16H30/20 ,  G06V10/774 ,  G06T7/00

CPC classification number: G16H30/40 ,  G16H30/20 ,  G06V10/7753 ,  G06T7/0012 ,  G06T2207/30024 ,  G06V2201/03

Abstract: A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.

公开(公告)号：US20230410958A1

公开(公告)日：2023-12-21

申请号：US18334836

申请日：2023-06-14

Applicant: Lunit Inc.

Inventor： Kyung Hyun PAENG ,  Seung Yun Oh ,  Ji Min Moon ,  Se Jin Kim

IPC: G16H10/20 ,  G16H50/70 ,  G16H15/00 ,  G06T7/00 ,  G16B20/00

CPC classification number: G16H10/20 ,  G16H50/70 ,  G16H15/00 ,  G06T7/0016 ,  G16B20/00 ,  G16H30/20

Abstract: A computing apparatus includes at least one memory storing at least one program, and at least one processor configured to, by executing the at least one program, acquire at least one of first information regarding a primary clinical trial previously performed on a certain drug and second information indicating an association between the drug and each of candidate biomarkers, set a criterion related to responsitivity to the drug based on the acquired information, and generate information related to a secondary clinical trial based on the set criterion.

公开(公告)号：US20230386028A1

公开(公告)日：2023-11-30

申请号：US18321132

申请日：2023-05-22

Applicant: Lunit Inc.

Inventor： Ga Hee PARK ,  Kyung Hyun PAENG ,  Chan Young OCK ,  Sang Hoon SONG ,  Suk Jun KIM

IPC: G06T7/00 ,  G06V20/69 ,  G16H30/40 ,  G16H15/00

CPC classification number: G06T7/0012 ,  G06V20/698 ,  G16H30/40 ,  G16H15/00 ,  G06T2207/30096 ,  G06T2207/30204 ,  G06T2207/30168 ,  G06V2201/03 ,  G06T2207/30024

Abstract: A computing device includes at least one memory, and at least one processor configured to analyze at least one object expressed in a pathological slide image, evaluate quality of the pathological slide image based on a result of the analyzing, and perform at least one additional operation according to a result of the evaluating.

公开(公告)号：US20230206433A1

公开(公告)日：2023-06-29

申请号：US18056773

申请日：2022-11-18

Applicant: LUNIT INC.

Inventor： Ga Hee PARK ,  Chan Young OCK ,  Kyung Hyun PAENG

IPC: G06T7/00 ,  G06V20/69

CPC classification number: G06T7/0012 ,  G06V20/698 ,  G06V2201/03 ,  G06T2207/30024 ,  G06T2207/30096 ,  G06T2207/30242 ,  G06T2207/10056

Abstract: Provided is a computing apparatus including: at least one memory; and at least one processor, wherein the at least one processor is configured to: perform a first classification on a plurality of tissues expressed in a pathological slide image by analyzing the pathological slide image, perform a second classification on a plurality of cells expressed in a pathological slide image by analyzing the pathological slide image, and calculate tumor purity including information on noise included in the pathological slide image by combining a first classification result and a second classification result.

公开(公告)号：US20200152316A1

公开(公告)日：2020-05-14

申请号：US16671430

申请日：2019-11-01

Applicant: Lunit Inc.

Inventor： Kyoung Won LEE ,  Kyung Hyun PAENG

IPC: G16H30/40 ,  G06F17/24 ,  G16H30/20 ,  G06N3/08 ,  G06K9/62

Abstract: A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.