公开(公告)号：US11620529B2

公开(公告)日：2023-04-04

申请号：US16706570

申请日：2019-12-06

Applicant: Lunit Inc.

Inventor： Hyo-Eun Kim

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/084

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.

公开(公告)号：US20230053280A1

公开(公告)日：2023-02-16

申请号：US17973672

申请日：2022-10-26

Applicant: LUNIT INC.

Inventor： Donggeun YOO ,  Sanghyup LEE ,  Minchul KIM ,  Hanjun LEE ,  Sunggyun PARK

IPC: A61B5/00 ,  A61B6/00

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：US20220262513A1

公开(公告)日：2022-08-18

申请号：US17550034

申请日：2021-12-14

Applicant: LUNIT INC.

Inventor： Donggeun YOO ,  Jaehong AUM ,  Minuk MA ,  Jeong Un RYU

IPC: G16H50/20 ,  G06N20/00 ,  G16H30/20

Abstract: A method, performed by at least one processor, for training a machine learning model for detecting an abnormal region in a pathological slide image is disclosed. The method including receiving one or more first pathological slide images, determining, from the received one or more first pathological slide images, a normal region based on an abnormality condition indicative of a condition of an abnormal region, generating a first set of training data including the determined normal region, generating the abnormal region by performing image processing corresponding to the abnormality condition with respect to at least partial region in the received one or more first pathological slide images, and generating a second set of training data including the generated abnormal region.

公开(公告)号：US20220261988A1

公开(公告)日：2022-08-18

申请号：US17551506

申请日：2021-12-15

Applicant: LUNIT INC.

Inventor： Donggeun YOO ,  Jaehong AUM ,  Minuk MA ,  Jeong Un RYU

IPC: G06T7/00 ,  G16H10/40 ,  G16H70/60 ,  G16H30/20 ,  G16H30/40

Abstract: A method for detecting a region of interest (ROI) in a pathological slide image is provided. The method may include receiving one or more pathological slide images and detecting an ROI in the received one or more pathological slide images. In addition, an information processing system is provided. The information processing system includes a memory storing one or more instructions, and a processor configured to execute the stored one or more instructions to receive one or more pathological slide images and detect an ROI in the received one or more pathological slide images.

公开(公告)号：US20220237793A1

公开(公告)日：2022-07-28

申请号：US17721515

申请日：2022-04-15

Applicant: Lunit Inc.

Inventor： Hyo-Eun KIM ,  Hyeonseob Nam

IPC: G06T7/00 ,  G06N3/08 ,  G06V10/40

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.

公开(公告)号：US11334994B2

公开(公告)日：2022-05-17

申请号：US16874926

申请日：2020-05-15

Applicant: Lunit Inc.

Inventor： Hyo-Eun Kim ,  Hyeonseob Nam

IPC: G06K9/00 ,  G06T7/00 ,  G06N3/08 ,  G06V10/40

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.

公开(公告)号：US20210391059A1

公开(公告)日：2021-12-16

申请号：US17077142

申请日：2020-10-22

Applicant: Lunit Inc.

Inventor： Jongchan PARK

IPC: G16H30/40 ,  G16H50/20 ,  G06T7/00

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.

公开(公告)号：US20210295151A1

公开(公告)日：2021-09-23

申请号：US17077114

申请日：2020-10-22

Applicant: Lunit Inc.

Inventor： Donggeun YOO ,  Jeong Hoon LEE ,  Jae Hwan LEE

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62 ,  G06K9/46

Abstract: There is provided a method and apparatus that collects feature points of data and performs machine learning. A machine learning method comprises receiving first feature data obtained by applying a basic model to first analysis target data, receiving second feature data obtained by applying the basic model to second analysis target data, and obtaining a final machine learning model through performing machine learning on a correlation between the first feature data and first analysis result data and a correlation between the second feature data and second analysis result data.

公开(公告)号：US11062800B2

公开(公告)日：2021-07-13

申请号：US16814131

申请日：2020-03-10

Applicant: Lunit Inc.

Inventor： Kyoung Won Lee ,  Kyung Hyun Paeng

IPC: G16H30/40 ,  G06K9/62 ,  G06N3/08 ,  G16H30/20 ,  G06F40/169 ,  G16H40/20 ,  G16H10/40 ,  G06Q10/06 ,  G06T7/00

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：US10922628B2

公开(公告)日：2021-02-16

申请号：US16684627

申请日：2019-11-15

Applicant: Lunit Inc.

Inventor： Dong Geun Yoo ,  Kyung Hyun Paeng ,  Sung Gyun Park

IPC: G06N20/00 ,  G06N20/20 ,  G06F11/34 ,  G06F17/18

Abstract: A machine learning method that may reduce an annotation cost and may improve performance of a target model is provided. Some embodiments of the present disclosure may provide a machine learning method performed by a computing device, including: acquiring a training dataset of a first model including a plurality of data samples to which label information is not given; calculating a miss-prediction probability of the first model on the plurality of data samples; configuring a first data sample group by selecting at least one data sample from the plurality of data samples based on the calculated miss-prediction probability; acquiring first label information on the first data sample group; and performing first learning on the first model by using the first data sample group and the first label information.