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The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Patent No.:

US 12586184 B1

PDFFull Text
Date of Patent:
Mar. 24, 2026

Filed:

Mar. 07, 2023

Methods and apparatus for analyzing pathology patterns of whole-slide images based on graph deep learning

Applicants:

Seoul National University R&db Foundation, Seoul, KR;

Seoul National University Hospital, Seoul, KR;

Inventors:

Sunghoon Kwon, Seoul, KR;

Yongju Lee, Goyang-si, KR;

Kyoungseob Shin, Goyang-si, KR;

Kyung Chul Moon, Seoul, KR;

Jeong Hwan Park, Seoul, KR;

Sohee Oh, Seoul, KR;

Assignees:

SEOUL NATIONAL UNIVERSITY R&DB FOUNDATION, Seoul, KR;

SEOUL NATIONAL UNIVERSITY HOSPITAL, Seoul, KR;

Attorney:

STIP Law Group, LLC

Primary Examiner:

Henok Shiferaw

Assistant Examiner:

Dennis undefined Rosario

Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 8/00 (2006.01); G06N 3/02 (2006.01); G06N 3/0464 (2023.01); G06N 3/08 (2023.01); G06N 5/04 (2023.01); G06N 5/045 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/162 (2017.01); G06T 7/33 (2017.01); G06V 10/26 (2022.01); G06V 10/40 (2022.01); G06V 10/426 (2022.01); G06V 10/44 (2022.01); G06V 10/50 (2022.01); G06V 10/70 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/84 (2022.01); G06V 20/40 (2022.01); G06V 20/69 (2022.01); G06V 40/12 (2022.01); G16H 30/40 (2018.01); G16H 50/00 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 8/5223 (2013.01); G06N 3/02 (2013.01); G06N 3/0464 (2023.01); G06N 3/08 (2013.01); G06N 5/04 (2013.01); G06N 5/045 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/162 (2017.01); G06T 7/337 (2017.01); G06V 10/26 (2022.01); G06V 10/40 (2022.01); G06V 10/426 (2022.01); G06V 10/44 (2022.01); G06V 10/50 (2022.01); G06V 10/70 (2022.01); G06V 10/762 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/84 (2022.01); G06V 20/46 (2022.01); G06V 20/69 (2022.01); G06V 20/695 (2022.01); G06V 40/1347 (2022.01); G16H 30/40 (2018.01); G16H 50/00 (2018.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01); G06T 2207/10056 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20072 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01); G06T 2207/30024 (2013.01); G06T 2210/41 (2013.01); G06V 2201/03 (2022.01);
Abstract

The present invention relates to a method and apparatus for analyzing pathology patterns of whole-slide images based on graph deep learning, which may include: a whole-slide image (WSI) compression step of compressing WSI into a superpatch graph; a graph neural networks (GNN) analysis step of embedding node features and context features into the superpatch graph through a GNN model and calculating contributions for each node and edge; a biomarker acquisition step of classifying and grouping the superpatch graph according to the contributions for each node, connecting the classified and grouped superpatch graph in units of groups to generate connected graphs, normalizing and clustering features of the connected graphs, and acquiring environmental graph biomarkers for each group; and a diagnostic information extraction step of extracting and providing diagnostic information on the WSI based on the environmental graph biomarker for each group.

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