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:
Nov. 26, 2019

Filed:

Dec. 01, 2017
Applicants:

Shanghai Sixth People's Hospital, Shanghai, CN;

Shanghai Jiao Tong University, Shanghai, CN;

Inventors:

Weiping Jia, Shanghai, CN;

Bin Sheng, Shanghai, CN;

Huating Li, Shanghai, CN;

Ling Dai, Shanghai, CN;

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G06T 7/00 (2017.01); G06N 3/04 (2006.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G16H 15/00 (2018.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G06K 9/00147 (2013.01); G06N 3/04 (2013.01); G06N 3/0454 (2013.01); G06T 7/0012 (2013.01); G16H 30/40 (2018.01); G16H 50/20 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30041 (2013.01); G06T 2207/30096 (2013.01); G06T 2207/30101 (2013.01); G16H 15/00 (2018.01);
Abstract

A method of automatically detecting microaneurysm based on multi-sieving convolutional neural network (CNN), includes the following steps of: A), partitioning an image to be detected using random fern and obtaining an auxiliary channel image of the image according to a first partition result; and A), inputting the auxiliary channel image obtained from step A) and the image to a multi-sieving CNN training model to perform a detection and obtaining a microaneurysm detection result of the image. The process of establishing the training model includes: B), using a current microaneurysm diagnostic report as samples and partitioning a lesion image in the microaneurysm diagnostic report using the random fern, and establishing the auxiliary channel image according to a second partition result; B), comparing the obtained auxiliary channel image with a lesion-marked image of pixels, clarifying the samples according to a comparing result and performing the multi-sieving CNN training.


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