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.

Date of Patent:
Jul. 13, 2021

Filed:

Mar. 10, 2020
Applicant:

Lunit Inc., Seoul, KR;

Inventors:

Kyoung Won Lee, Seoul, KR;

Kyung Hyun Paeng, Busan, KR;

Assignee:

LUNIT INC., Seoul, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G16H 30/40 (2018.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01); G16H 30/20 (2018.01); G06F 40/169 (2020.01); G16H 40/20 (2018.01); G16H 10/40 (2018.01); G06Q 10/06 (2012.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06F 40/169 (2020.01); G06K 9/6262 (2013.01); G06N 3/08 (2013.01); G06Q 10/06395 (2013.01); G06Q 10/063112 (2013.01); G06T 7/0012 (2013.01); G16H 10/40 (2018.01); G16H 30/20 (2018.01); G16H 40/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30024 (2013.01); G06T 2207/30096 (2013.01);
Abstract

A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.


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