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. 03, 2018

Filed:

Sep. 08, 2015
Applicant:

Lunit Inc., Seoul, KR;

Inventors:

Hyo-eun Kim, Seoul, KR;

Sang-heum Hwang, Seoul, KR;

Seung-wook Paek, Seoul, KR;

Jung-in Lee, Seoul, KR;

Min-hong Jang, Seoul, KR;

Dong-geun Yoo, Daejeon, KR;

Kyung-hyun Paeng, Busan, KR;

Sung-gyun Park, Gyeonggi-do, KR;

Assignee:

LUNIT INC., Seoul, KR;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2017.01); G06F 19/00 (2018.01); G06F 17/30 (2006.01); G06N 99/00 (2010.01); G06K 9/48 (2006.01); G06K 9/62 (2006.01); G06K 9/66 (2006.01); G06T 7/11 (2017.01); A61B 8/08 (2006.01); G16H 50/20 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); G06F 17/30244 (2013.01); G06F 19/321 (2013.01); G06K 9/481 (2013.01); G06K 9/6267 (2013.01); G06K 9/66 (2013.01); G06N 99/005 (2013.01); G06T 7/11 (2017.01); A61B 8/0825 (2013.01); G06F 19/345 (2013.01); G06K 2209/05 (2013.01); G06T 2207/20021 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30096 (2013.01); G16H 50/20 (2018.01);
Abstract

The present invention relates to a classification apparatus for pathologic diagnosis of a medical image and a pathologic diagnosis system using the same. According to the present invention, there is provided a classification apparatus for pathologic diagnosis of a medical image, including: a feature extraction unit configured to extract feature data for an input image using a feature extraction variable; a feature vector transformation unit configured to transform the extracted feature data into a feature vector using a vector transform variable; and a vector classification unit configured to classify the feature vector using a classification variable, and to output the results of the classification of pathologic diagnosis for the input image; wherein the feature extraction unit, the feature vector transformation unit and the vector classification unit are trained based on a first tagged image, a second tagged image, and an image having no tag information.


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