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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.

Patent No.:

US 11961227 B1

PDFFull Text
Date of Patent:
Apr. 16, 2024

Filed:

Feb. 05, 2021

Method and device for detecting and locating lesion in medical image, equipment and storage medium

Applicant:

Ping an Technology (Shenzhen) Co., Ltd., Guangdong, CN;

Inventors:

Yue Wang, Guangdong, CN;

Bin Lv, Guangdong, CN;

Chuanfeng Lv, Guangdong, CN;

Assignee:

Ping An Technology (Shenzhen) Co., Ltd., Guangdong, CN;

Attorney:

IP Spring

Primary Examiner:

Zhitong Chen

Int. Cl.
CPC ...
G06T 7/00 (2017.01); A61B 5/00 (2006.01); A61B 6/03 (2006.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01); G06F 18/25 (2023.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06T 7/187 (2017.01); G06T 7/73 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06T 7/0012 (2013.01); A61B 5/0066 (2013.01); A61B 5/4887 (2013.01); A61B 5/7267 (2013.01); A61B 5/7275 (2013.01); A61B 6/032 (2013.01); G06F 18/2148 (2023.01); G06F 18/217 (2023.01); G06F 18/2415 (2023.01); G06F 18/253 (2023.01); G06T 7/11 (2017.01); G06T 7/136 (2017.01); G06T 7/187 (2017.01); G06T 7/73 (2017.01); G06V 10/764 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/806 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/30096 (2013.01); G06V 2201/03 (2022.01);
Abstract

A method for detecting and locating a lesion in a medical image is provided. A target medical image of a lesion is obtained and input into a deep learning model to obtain a target sequence. A first feature map output from the last convolution layer in the deep learning model is extracted. A weight value of each network unit corresponding to each preset lesion type in a fully connected layer is extracted. For each preset lesion type, a fusion feature map is calculated according to the first feature map and the corresponding weight value and resampled to the size of the target medical image to generate a generic activation map. The maximum connected area in each generic activation map is determined, and a mark border surrounding the maximum connected area is created. A mark border corresponding to each preset lesion type is added to the target medical image.

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