In this paper, we propose a novel graph attention U-Net (GA-UNet) for retinal layer surface detection and CNV segmentation in optical coherence tomography (OCT ...
In this paper, we propose a novel graph attention U-Net (GA-UNet) for retinal layer surface detection and CNV segmentation in optical coherence tomography (OCT) ...
Feb 3, 2024 · The proposed model learns graph attention maps automatically during training and performs retinal layer surface detection and CNV segmentation ...
The Graph-Cut technique algorithm is used for both normal participants' and diabetic individuals' OCT images. The layers of the segmented OCT retina are used to ...
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This paper presents a novel U-Net model incorporating a hybrid attention mechanism for automating the segmentation of sub-retinal layers in Optical Coherence ...
Automatic segmentation of retinal layer boundaries in OCT images using multiscale convolutional neural network and graph search
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Graph Attention U-Net for Retinal Layer Surface Detection and Choroid Neovascularization Segmentation in OCT Images · Medicine, Engineering. IEEE Transactions on ...
Graph Attention U-Net for Retinal Layer Surface Detection and Choroid Neovascularization Segmentation in OCT Images. Article. Jan 2023. Yuhe Shen ...
A deep learning algorithm for multiclass semantic segmentation of retinal layers in Optical Coherence Tomography images.
![PDF] Automatic Segmentation of Retinal Layer in OCT Images With ...](https://www.google.com/imgres?imgurl=https://d3i71xaburhd42.cloudfront.net/79829e975908d3fc456f18b6d113968521ebc214/1-Figure1-1.png&imgrefurl=https://www.semanticscholar.org/paper/Automatic-Segmentation-of-Retinal-Layer-in-OCT-With-Xiang-Tian/79829e975908d3fc456f18b6d113968521ebc214&h=308&w=692&tbnid=vWmzC_mjJcbTBM&q=Graph+Attention+U-Net+for+Retinal+Layer+Surface+Detection+and+Choroid+Neovascularization+Segmentation+in+OCT+Images.&tbnh=62&tbnw=139&usg=AI4_-kQ9nAL8TgN7_yHiKwluSeHAhb6pHA&vet=1&docid=lW1CJvcWd3BWLM&itg=1&sa=X&ved=2ahUKEwjsl4bnudGIAxUNKUQIHUugL10Q9QF6BAgBEAg)
