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. 14, 2025

Filed:

Aug. 10, 2023
Applicant:

Hangzhou Dianzi University, Hangzhou, CN;

Inventors:

Bishi He, Hangzhou, CN;

Zhe Xu, Hangzhou, CN;

Yuanjiao Chen, Hangzhou, CN;

Diao Wang, Hangzhou, CN;

Hui Chen, Hangzhou, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/764 (2022.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06V 10/765 (2022.01); G06T 7/0012 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30061 (2013.01); G06V 2201/031 (2022.01);
Abstract

A Wasserstein distance and difference metric-combined chest radiograph anomaly identification domain adaptation method and a corresponding system are provided. The method includes the following steps: step 1, data preparation and data pre-processing for chest radiographs; step 2, multi-scale feature extraction based on a swin transformer network; step 3, loss minimization based on a Wasserstein distance and a contrastive domain discrepancy; and step 4, using the model to perform chest radiograph prediction after verifying the model. The method selects source domain samples closest to target domain samples, narrows a distance of the same class between the target domain samples and the source domain samples in feature space, and expands a distance between different classes. Meanwhile, a classification prediction task for the chest radiographs is performed by using the multi-scale features, improving a receptive field and capturing more information conducive to the classification prediction task.


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