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公开(公告)号：US12169933B2
公开(公告)日：2024-12-17
申请号：US17704879
申请日：2022-03-25
Inventor： JongChul Ye , Sangjoon Park , Gwanghyun Kim
IPC: G06T7/00 , A61B6/00 , G06T7/10 , G06T7/73 , G06V10/32 , G06V10/764 , G06V10/77 , G06V10/774 , G06V10/82
Abstract: Disclosed is a method and apparatus for quantifying severity of infectious disease based on a vision transformer using a chest X-ray (CXR) image. Here, a method of quantifying severity of infectious disease based on a vision transformer includes receiving an input CXR image; extracting a feature map from the received input CXR image using a pretrained neural network; classifying a lesion in the input CXR image using the vision transformer based on the extracted feature map; and quantifying severity of the input CXR image based on the extracted feature map and the classified lesion.
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公开(公告)号：US20220414954A1
公开(公告)日：2022-12-29
申请号：US17848689
申请日：2022-06-24
Inventor： JongChul Ye , Taesung Kwon
Abstract: Disclosed are a method and apparatus for processing a low-dose X-ray computed tomography image based on efficient unsupervised learning by using an invertible neural network. The method of processing a low-dose X-ray computed tomography image based on unsupervised learning by using an invertible neural network performed by a computer device includes providing an invertible generator for restoring an image, and training the invertible generator to restore a low-dose computed tomography image to a normal computed tomography image.
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公开(公告)号：US20220358691A1
公开(公告)日：2022-11-10
申请号：US17711944
申请日：2022-04-01
Inventor： JongChul Ye , Gyutaek Oh
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.
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公开(公告)号：US20220309661A1
公开(公告)日：2022-09-29
申请号：US17704879
申请日：2022-03-25
Inventor： JongChul Ye , Sangjoon Park , Gwanghyun Kim
IPC: G06T7/00 , G06V10/764 , G06V10/77 , G06V10/32 , G06T7/73 , G06T7/10 , G06V10/774 , G06V10/82 , A61B6/00
Abstract: Disclosed is a method and apparatus for quantifying severity of infectious disease based on a vision transformer using a chest X-ray (CXR) image. Here, a method of quantifying severity of infectious disease based on a vision transformer includes receiving an input CXR image; extracting a feature map from the received input CXR image using a pretrained neural network; classifying a lesion in the input CXR image using the vision transformer based on the extracted feature map; and quantifying severity of the input CXR image based on the extracted feature map and the classified lesion.
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公开(公告)号：US12223433B2
公开(公告)日：2025-02-11
申请号：US17332696
申请日：2021-05-27
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.
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Patent Agency Ranking
公开(公告)号：US12056880B2
公开(公告)日：2024-08-06
申请号：US17352229
申请日：2021-06-18
Inventor： JongChul Ye , Sangjoon Park , Yujin Oh , Gwanghyun Kim
CPC classification number: G06T7/143 , G06T7/11 , G06T2207/10116 , G06T2207/20004 , G06T2207/20076 , G06T2207/20081 , G06T2207/20084 , G06T2207/30061
Abstract: Disclosed are a method of classifying lesions of chest x-ray radiographs based on data normalization and local patches and an apparatus thereof. The method includes converting an input chest x-ray radiograph into a normalized image, segmenting the converted normalized image into an organ area by using a first neural network based on a pre-learned segmentation model, generating local patches for the segmented organ area, and classifying a lesion in the input chest x-ray radiograph by using a second neural network based on a pre-learned classification model for the generated local patches.
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公开(公告)号：US11478225B2
公开(公告)日：2022-10-25
申请号：US16721015
申请日：2019-12-19
Inventor： JongChul Ye , Shujaat Khan , Jaeyoung Huh
Abstract: An apparatus and method for processing an ultrasound image in various sensor conditions are provided. The method includes receiving a data cube via sensors, transforming the received data cube into focus data through beam focusing, and outputting inphase data and quadrature phase data for the focus data using a neural network corresponding to signal adder and Hilbert transform functions. The method further includes detecting an envelope of the inphase data and the quadrature phase data and reconstructing an ultrasound image for the data cube using log compression.
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公开(公告)号：US20220005150A1
公开(公告)日：2022-01-06
申请号：US17365598
申请日：2021-07-01
Inventor： JongChul Ye , Boah Kim
Abstract: Disclosed are an unsupervised learning-based image registration method using a neural network with cycle consistency and an apparatus therefor. An image registration method includes receiving a first image and a second image for image registration, outputting a deformation field for the first image and the second image using an unsupervised learning-based neural network with cycle consistency for the deformation field, and generating a registration image for the first image and the second image based on a spatial deformation function using the output deformation field. The outputting of the deformation field includes outputting the deformation field for the first image for registering the first image to the second image may be output, when the first image is a moving image and the second image is a fixed image, and the generating of the registration image includes generating the registration image by applying the deformation field for the first image to the first image using the spatial deformation function.
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9.发明授权公开(公告)号：US11145028B2
公开(公告)日：2021-10-12
申请号：US16414372
申请日：2019-05-16
Inventor： JongChul Ye , Yeohun Yoon , Shujaat Khan , Jaeyoung Huh
Abstract: An image processing apparatus according to an embodiment removes the noise included in the three-dimensional input image, determines the lost information in a process of obtaining the three-dimensional input image, or enhances the resolution of the three-dimensional input image, by using the neural network learned in advance. The image processing apparatus slices the three-dimensional input image along a depth, converts a three-dimensional input image into a two-dimensional input image, and inputs the converted two-dimensional input image into a neural network. The image processing apparatus generates a three-dimensional output image of which the quality of the three-dimensional input image is enhanced, based on the output of the neural network.
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公开(公告)号：US11037336B2
公开(公告)日：2021-06-15
申请号：US16409541
申请日：2019-05-10
Applicant: Korea Advanced Institute of Science and Technology , The Asan Foundation , University of Ulsan Foundation For Industry Cooperation
Inventor： JongChul Ye , Eunhee Kang , Dong Hyun Yang , Joon Bum Seo , Hyun Jung Koo
Abstract: A method for processing an unmatched low-dose X-ray computed tomography (CT) image using a neural network and an apparatus therefor are provided. The method includes receiving a low-dose X-ray CT image and removing noise from the low-dose X-ray CT image using a unsupervised learning based neural network learned using unmatched data to reconstruct a routine-dose X-ray CT image corresponding to the low-dose X-ray CT image.
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