Xerophthalmia grading system based on regional adaptive attention network

A grading system, dry eye technology, applied in the field of medical image analysis and machine learning, can solve problems such as low accuracy and low efficiency, and achieve the effect of improving accuracy, speed, and diagnostic efficiency

Active Publication Date: 2020-01-24
ZHEJIANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the shortcomings of the existing dry eye classification that requires multiple detection aids, low efficiency, and low precision, the present invention proposes a fast, high-efficiency, and high-pre

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  • Xerophthalmia grading system based on regional adaptive attention network
  • Xerophthalmia grading system based on regional adaptive attention network
  • Xerophthalmia grading system based on regional adaptive attention network

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[0028] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be pointed out that the embodiments described below are intended to facilitate the understanding of the present invention and do not have any limiting effect on it.

[0029] This embodiment provides a dry eye classification system based on a regional adaptive attention network, including a computer memory, a computer processor, and a computer program stored in the computer memory and executable on the computer processor. The computer memory stores There is a trained dry eye grading model, which is obtained through the following three stages:

[0030] Phase 1: Construction of the training set

[0031] The infrared image of the eyelid plate used in this embodiment is divided into upper and lower tarsal plates, which respectively contain 4 levels of dry eye, including: no dry eye, mild, moderate and severe dry eye. There are 11,584 samples of the...

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Abstract

The invention discloses a xerophthalmia grading system based on a regional adaptive attention network. The system comprises a computer memory, a computer processor and a computer program which is stored in the computer memory and can be executed on the computer processor, a trained xerophthalmia grading model is stored in the computer memory, and the xerophthalmia grading model is based on a regional adaptive attention network; when the computer processor executes the computer program, the following steps are realized: obtaining a to-be-tested original eyelid plate infrared image to carry outgray scale preprocessing, and carrying out bilateral filtering processing on a single-channel gray scale image obtained after preprocessing; and inputting the processed image into a xerophthalmia grading model to obtain an eyelid plate positioning and xerophthalmia grading result. According to the invention, automatic analysis of the eyelid plate infrared photograph can be realized, and auxiliarydiagnosis of xerophthalmia grading can be effectively carried out.

Description

technical field [0001] The invention belongs to the field of medical image analysis and machine learning, and in particular relates to a dry eye syndrome grading system based on an area self-adaptive attention network. Background technique [0002] Dry eye is a multifactorial disease of the ocular surface, characterized by loss of tear film homeostasis, accompanied by ocular surface symptoms, and its pathogenesis includes tear film instability, tear hyperosmolarity, ocular surface inflammation and loss, and neurosensory abnormalities. Many studies have shown that the incidence of dry eye fluctuates from 5 to 50%. [0003] The traditional way of diagnosing and evaluating dry eye is to use ocular surface disease index (Ocular Surface Disease Index, OSDI), NIBUT, tear river height, meibomian gland morphology and other indicators, which can objectively diagnose and classify dry eye. However, the shortcomings of this evaluation method are: there are many relevant evaluation indi...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46G06T7/00G06N3/08
CPCG06T7/0012G06N3/08G06T2207/20028G06T2207/20081G06T2207/20084G06T2207/30041G06V10/40G06V2201/03G06F18/241G06F18/214
Inventor 徐雯吴健林琳陆逸飞晋秀明黄晓丹吴星镝罗陈启马雅娟陈香
Owner ZHEJIANG UNIV
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