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:
Sep. 08, 2026

Filed:

Jun. 22, 2022
Applicants:

Ankon Technologies Co., Ltd, Wuhan, CN;

Anx Ip Holding Pte. Ltd., Singapore, SG;

Inventors:

Chukang Zhang, Wuhan, CN;

Zhiwei Huang, Wuhan, CN;

Hao Zhang, Wuhan, CN;

Fanhua Ming, Wuhan, CN;

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); G06V 10/74 (2022.01); G16H 50/30 (2018.01); G16H 50/70 (2018.01);
U.S. Cl.
CPC ...
G16H 50/30 (2018.01); G06T 7/0012 (2013.01); G06V 10/74 (2022.01); G16H 50/70 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30092 (2013.01); G06V 2201/032 (2022.01);
Abstract

The present invention provides a deep learning-based cancer prognosis survival prediction method and a device, and a storage medium. The method includes: data acquisition: acquiring sample data, where the sample data comprising pathological image data and clinical data of a sample; data preprocessing; prediction model training: training and evaluating a prediction model to obtain an optimal prediction model; and risk prediction: performing risk prediction on a new sample on the basis of an optimal classifier model and the optimal prediction model. In the method, data features of the pathological image data and clinical data are unified, the prediction model is trained and evaluated to obtain the optimal prediction model, and prognosis risk evaluation is performed on the new sample to enhance the efficiency of diagnosis and treatment in the clinical field and improve the accuracy of risk evaluation results.


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