公开(公告)号：US20220036564A1

公开(公告)日：2022-02-03

申请号：US17352229

申请日：2021-06-18

Applicant: Korea Advanced Institute of Science and Technology

Inventor： JongChul Ye ,  Sangjoon Park ,  Yujin Oh ,  Gwanghyun Kim

IPC: G06T7/143 ,  G06T7/11

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.

公开(公告)号：US12056880B2

公开(公告)日：2024-08-06

申请号：US17352229

申请日：2021-06-18

Applicant: Korea Advanced Institute of Science and Technology

Inventor： JongChul Ye ,  Sangjoon Park ,  Yujin Oh ,  Gwanghyun Kim

IPC: G06T7/143 ,  G06T7/11

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.

公开(公告)号：US12169933B2

公开(公告)日：2024-12-17

申请号：US17704879

申请日：2022-03-25

Applicant: Korea Advanced Institute of Science and Technology

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.

公开(公告)号：US20220309661A1

公开(公告)日：2022-09-29

申请号：US17704879

申请日：2022-03-25

Applicant: Korea Advanced Institute of Science and Technology

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.