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AMD grading system based on macular attention mechanism and uncertainty

A grading system and attention-based technology, applied in the field of macular disease detection, can solve problems such as the inability of the macular disease detection system to give the accuracy of detection results and potential safety hazards

Pending Publication Date: 2021-03-05
中科泰明(南京)科技有限公司
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  • Application Information

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Problems solved by technology

[0006] Aiming at the deficiencies of the prior art, the present invention provides an AMD grading system based on macular attention mechanism and uncertainty, which solves the problem that the existing macular disease detection system based on deep learning algorithm cannot provide the accuracy of detection results, Issues with potential safety hazards

Method used

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  • AMD grading system based on macular attention mechanism and uncertainty
  • AMD grading system based on macular attention mechanism and uncertainty

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Embodiment 1

[0059] like figure 1 Shown, the invention provides a kind of AMD grading system based on macular attention mechanism and uncertainty, said system comprises:

[0060] The blood vessel optic disc segmentation module is used to segment the fundus color map to obtain binarized optic disc segmentation images and blood vessel segmentation images;

[0061] A macular region positioning module, configured to obtain a macular region image based on the optic disc segmentation image and blood vessel segmentation image;

[0062] The classification network module is used to use the fundus color map and the corresponding optic disc segmentation image, blood vessel segmentation image, and macular area image as the input of the trained deep learning classification network model based on the attention mechanism, and use the attention network to combine the fundus color map and The corresponding blood vessel segmentation image and macular area image are fused to obtain a multi-channel image, an...

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Abstract

The invention provides an AMD grading system based on a macular attention mechanism and uncertainty, and relates to the technical field of macular disease detection. An optic disc segmentation image and a blood vessel segmentation image are obtained by using a segmentation network model, a macular area image is obtained according to the obtained optic disc segmentation image and the blood vessel segmentation image, a multichannel image is obtained by using an attention network sub-module, features are extracted by using a Bayesian deep learning classification network model, and through multiple times of dropout Monte Carlo, four groups of probability values and one group of noise corresponding to the four lesion types are output; and the classification network module gives out accidental uncertainty and model uncertainty while finally outputting a model classification result. And the safety performance of the model is ensured.

Description

technical field [0001] The invention relates to the technical field of macular disease detection, in particular to an AMD grading system based on macular attention mechanism and uncertainty. Background technique [0002] The macula refers to a circular area with a diameter of about 5-6 mm between the posterior pole and the temporal vascular arch. Diseases caused by various reasons and occurring in this area are called macular diseases. Macular diseases, such as age-related macular degeneration (Age-Related Macular Degeneration, AMD), have seriously affected the quality of life and work of millions of people around the world. There are many types of diseases and complex characteristics, and it is difficult to diagnose in the early stage of the disease. Without timely diagnosis and appropriate treatment, these macular diseases will lead to irreversible blurred vision, metamorphopsia, visual field defect, and in the worst case, blindness. [0003] Existing macular disease dete...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/11G06T5/30G06T5/50G06K9/62G06N3/04G06N3/08
CPCG06T7/0012G06T7/11G06T5/30G06T5/50G06N3/08G06T2207/20081G06T2207/20084G06T2207/20221G06T2207/30101G06N3/045G06F18/24155G06F18/214
Inventor 刘磊
Owner 中科泰明(南京)科技有限公司
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