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:
Oct. 01, 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;

Da-Wei Chang, Taipei, TW;

Assignee:
Attorney:
Primary Examiner:
Assistant 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 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 second model of a first analysis module and a third model of a second analysis module, respectively, to obtain at least one first prediction value and at least one second prediction value corresponding to the patient image; and outputting a determined result based on the first prediction value and the second prediction value. Further, processes between the first model, the second model and the third model can be automated, thereby improving identification rate of pancreatic cancer.


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