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：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

IPC: G06N3/08 ,  G06N3/04

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：US20210125074A1

公开(公告)日：2021-04-29

申请号：US16842435

申请日：2020-04-07

Applicant: Lunit Inc.

Inventor： HyunJae LEE ,  Hyo-Eun KIM ,  Weonsuk LEE

IPC: G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06T5/50

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：US20240193774A1

公开(公告)日：2024-06-13

申请号：US18584049

申请日：2024-02-22

Applicant: Lunit Inc.

Inventor： Hyo-Eun KIM ,  Hyeonseob NAM

IPC: G06T7/00 ,  G06N3/08 ,  G06V10/40 ,  G06V10/764

CPC classification number: G06T7/0012 ,  G06N3/08 ,  G06V10/40 ,  G06V10/764 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30068 ,  G06T2207/30096

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：US20200321118A1

公开(公告)日：2020-10-08

申请号：US16698878

申请日：2019-11-27

Applicant: Lunit Inc.

Inventor： Hyo-Eun KIM ,  HyunJae LEE

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

Abstract: A domain adaptation method and apparatus based on adversarial learning are provided. The method may include extracting feature data from multiple data sets, training a first discriminator discriminating a domain for a first class using first feature data extracted from a first data set corresponding to a first class of a first domain among the multiple data sets and training the first discriminator using second feature data extracted from a second data set corresponding to the first class of a second domain among the multiple data sets. The method may also include training a second discriminator discriminating a domain for a second class using third feature data extracted from a third data set that corresponds to a second class of the first domain, and training the second discriminator using fourth feature data extracted from a fourth data set that corresponds to the second class of the second domain.

公开(公告)号：US20230252626A1

公开(公告)日：2023-08-10

申请号：US18128492

申请日：2023-03-30

Applicant: Lunit Inc.

Inventor： Hyo-Eun KIM ,  Hyeonseob Nam

IPC: G06T7/00 ,  G06N3/08 ,  G06V10/40 ,  G06V10/764

CPC classification number: G06T7/0012 ,  G06N3/08 ,  G06V10/40 ,  G06V10/764 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30068 ,  G06T2207/30096

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：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.