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:
Apr. 26, 2022

Filed:

Oct. 29, 2020
Applicant:

Tencent Technology (Shenzhen) Company Limited, Shenzhen, CN;

Inventors:

Chen Cheng, Shenzhen, CN;

Zhongqian Sun, Shenzhen, CN;

Hao Chen, Shenzhen, CN;

Wei Yang, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G16H 30/40 (2018.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01); G06K 9/62 (2022.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
G16H 30/40 (2018.01); G06K 9/6232 (2013.01); G06K 9/6256 (2013.01); G06K 9/6267 (2013.01); G06N 3/08 (2013.01); G16H 30/20 (2018.01); G16H 50/20 (2018.01);
Abstract

The present application discloses a detection model training method and apparatus. The method includes determining an initial training model; determining a training sample; determining whether a lesion target is present in a first user body organ image through the initial detection model according to a feature of the each first user body organ image, to obtain a detection result; and determining a domain that each user body organ image in the training sample belongs to through the adaptive model according to a feature of the each user body organ image, to obtain a domain classification result; calculating, a loss function value related to the initial training model according to the detection result, the domain classification result, a first identifier, a second identifier, and a third identifier; and adjusting a parameter value in the initial training model according to the loss function value, to obtain a final detection model.


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