公开(公告)号：US20220358691A1

公开(公告)日：2022-11-10

申请号：US17711944

申请日：2022-04-01

Applicant: Korea Advanced Institute of Science and Technology

Inventor： JongChul Ye ,  Gyutaek Oh

IPC: G06T11/00 ,  G06N3/08 ,  A61B5/055 ,  G01R33/44 ,  G01R33/56

Abstract: Disclosed is a quantitative susceptibility mapping image processing method using an unsupervised learning-based neural network and an apparatus therefor. The quantitative susceptibility mapping image processing method includes receiving a phase image and a magnitude image for reconstructing the quantitative susceptibility mapping image, and reconstructing the quantitative susceptibility mapping image corresponding to the received phase image and the received magnitude image using an unsupervised learning-based neural network, and the neural network may be generated based on an optimal transport theory.

公开(公告)号：US20220027741A1

公开(公告)日：2022-01-27

申请号：US17332696

申请日：2021-05-27

Applicant: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： JongChul Ye ,  Byeongsu Sim ,  Gyutaek Oh

IPC: G06N3/08 ,  G06N3/04 ,  G06K9/62

Abstract: Disclosed are an unsupervised learning method and an apparatus therefor applicable to general inverse problems. An unsupervised learning method applicable to inverse problems includes receiving a training data set and training an unsupervised learning-based neural network generated based on an optimal transport theory and a penalized least square (PLS) approach using the training data set, wherein the receiving of the training data set includes receiving the training data set including unmatched data.

公开(公告)号：US12223433B2

公开(公告)日：2025-02-11

申请号：US17332696

申请日：2021-05-27

Applicant: KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY

Inventor： JongChul Ye ,  Byeongsu Sim ,  Gyutaek Oh

IPC: G06N3/088 ,  G06F18/214 ,  G06N3/045

Abstract: Disclosed are an unsupervised learning method and an apparatus therefor applicable to general inverse problems. An unsupervised learning method applicable to inverse problems includes receiving a training data set and training an unsupervised learning-based neural network generated based on an optimal transport theory and a penalized least square (PLS) approach using the training data set, wherein the receiving of the training data set includes receiving the training data set including unmatched data.