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:
Aug. 20, 2024

Filed:

Oct. 22, 2021
Applicant:

National Taiwan University, Taipei, TW;

Inventors:

Wei-Chung Wang, Taipei, TW;

Wei-Chih Liao, Taipei, TW;

Kao-Lang Liu, Taipei, TW;

Po-Ting Chen, Taipei, TW;

Po-Chuan Wang, Taipei, TW;

Ting-Hui Wu, Taipei, TW;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 5/00 (2006.01); G06N 3/08 (2023.01); G06T 7/70 (2017.01); G06T 7/73 (2017.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G16H 30/20 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/425 (2013.01); A61B 5/4887 (2013.01); A61B 5/726 (2013.01); A61B 5/7267 (2013.01); G06N 3/08 (2013.01); G06T 7/70 (2017.01); G06T 7/73 (2017.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G16H 30/20 (2018.01); G06T 2207/20016 (2013.01); G06T 2207/20064 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30204 (2013.01); G06V 2201/031 (2022.01);
Abstract

A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a first neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a plurality of second models of a second neural network module, respectively, to obtain a plurality of prediction values corresponding to each of the plurality of second models and a model number predicting having cancer in the plurality of prediction values; and outputting a determined result based on the model number predicting having cancer and a number threshold value. Further, processes between the first model and the second models can be automated, thereby improving identification rate of pancreatic cancer.


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