公开(公告)号：US20240062515A1

公开(公告)日：2024-02-22

申请号：US18260461

申请日：2021-11-09

Applicant: VUNO Inc.

Inventor： Hyunwoo OH ,  Sejin PARK ,  Eunpyeong HONG ,  Dongsoo LEE

IPC: G06V10/764 ,  G06N3/042 ,  G06N3/045 ,  G06T7/00 ,  G06V10/40 ,  G06V10/766 ,  G06V10/776 ,  G06V10/82

CPC classification number: G06V10/764 ,  G06N3/042 ,  G06N3/045 ,  G06T7/0012 ,  G06V10/40 ,  G06V10/766 ,  G06V10/776 ,  G06V10/82 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30016 ,  G06V2201/031

Abstract: According to an exemplary embodiment of the present disclosure, a method for classification by using a deep learning model, the method being performed by a computing device, is disclosed. The method may include: extracting a feature vector interpretable based on domain knowledge by inputting an image including at least one object of interest into a first neural network of a deep learning model; and estimating a probability value corresponding to a classification task by inputting the feature vector into a second neural network of the deep learning model. In this case, the deep learning model may be pre-trained based on a loss function having an output value of the first neural network and an output value of the second neural network as input variables.

公开(公告)号：US20220180512A1

公开(公告)日：2022-06-09

申请号：US17539827

申请日：2021-12-01

Applicant: VUNO Inc.

Inventor： Hyunwoo OH ,  Sejin PARK ,  Jinkyeong SUNG ,  Weon Jin KIM ,  Eunpyeong HONG ,  Dong Soo LEE

IPC: G06T7/00 ,  G06T7/30 ,  G06T7/11 ,  A61B5/00 ,  G06N3/04 ,  G16H30/40 ,  G16H50/20

Abstract: Disclosed is a method for predicting disease based on a medical image performed by a computing device. The method includes: generating a feature vector related to predictive values of brain disease for each of 2D medical images included in a 3D medical image, using a pre-trained first model; estimating importance indicating prediction accuracy for each of the 2D medical images based on the feature vector, using a pre-trained second model; and selecting at least one model input image suitable for prediction of the brain disease from among the 2D medical images based on the importance.

公开(公告)号：US20220130065A1

公开(公告)日：2022-04-28

申请号：US17509590

申请日：2021-10-25

Applicant: VUNO Inc.

Inventor： Eunpyeong HONG ,  Wonmo JUNG ,  Sejin PARK ,  Hyunwoo OH ,  Dong Soo LEE ,  Weon Jin KIM ,  Jinkyeong SUNG

IPC: G06T7/62 ,  G06T7/00 ,  G06T17/20

Abstract: Disclosed is a method for analyzing a thickness of a cortical region, performed by a computing device. The method may include: extracting a plurality of interfaces included in a cortical region based on a mask generated from a medical image including at least one brain region; and estimating a thickness of the cortical region based on the plurality of interfaces.

公开(公告)号：US20220172370A1

公开(公告)日：2022-06-02

申请号：US17533858

申请日：2021-11-23

Applicant: VUNO Inc. ,  SEOUL NATIONAL UNIVERSITY HOSPITAL

Inventor： Dong Soo LEE ,  Hyunwoo OH ,  Sejin PARK ,  Jinkyeong SUNG ,  Eunpyeong HONG ,  Weon Jin KIM ,  Ki Woong KIM ,  Jong Bin BAE ,  Subin LEE ,  Jun Sung KIM

IPC: G06T7/11

Abstract: According to an embodiment of the present disclosure, a method of detecting a white matter lesion based on a medical image performed by a computing device is disclosed. The method may include: receiving a medical image including at least one brain region; and estimating a first white matter lesion and a second white matter lesion based on the medical image using a pre-trained neural network model.

公开(公告)号：US20210295160A1

公开(公告)日：2021-09-23

申请号：US17207750

申请日：2021-03-22

Applicant: VUNO INC.

Inventor： Sejin PARK ,  Wonmo JEONG ,  Weonjin KIM

IPC: G06N3/08 ,  G16H30/00 ,  G06K9/52 ,  G16H20/10 ,  G06N3/063

Abstract: A method for a computing device to predict an action mechanism of a drug from medical images of a subject is disclosed. The method includes, from a plurality of medical images obtained in time series, outputting first compressed data corresponding to the plurality of medical images, each of the first compressed data having a smaller size than a corresponding medical image, estimating second compressed data corresponding to a medical image at a next time point to time points at which the plurality of medical images have been captured, based on the first compressed data, and predicting the action mechanism of the drug for the subject by inputting the second compressed data to a neural network predicting the action mechanism of the drug.