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Wfl-Vnet: Retinal Vessel Segmentation Method Using Whole-Process Feature Localization

https://doi.org/10.2139/ssrn.4676215
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inferredcites →https://doi.org/10.1002/anie.202009796openalex-extracted
Activity‐Based NIR Enzyme Fluorescent Probes for the Diagnosis of Tumors and Image‐Guided Surgery
inferredcites →https://doi.org/10.1007/978-3-031-09037-0_17openalex-extracted
DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet
inferredcites →https://doi.org/10.1007/978-3-031-25066-8_9openalex-extracted
Swin-Unet: Unet-Like Pure Transformer for Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-extracted
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s10489-020-01966-zopenalex-extracted
SAT-Net: a side attention network for retinal image segmentation
inferredcites →https://doi.org/10.1007/s10489-023-04773-4openalex-extracted
Curvilinear object segmentation in medical images based on ODoS filter and deep learning network
inferredcites →https://doi.org/10.1007/s10489-023-04939-0openalex-extracted
Retinal artery/vein classification by multi-channel multi-scale fusion network
inferredcites →https://doi.org/10.1016/j.bspc.2022.103930openalex-extracted
CSGNet: Cascade semantic guided net for retinal vessel segmentation
inferredcites →https://doi.org/10.1016/j.cmpb.2007.05.012openalex-extracted
Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images
inferredcites →https://doi.org/10.1016/j.compbiomed.2023.106960openalex-extracted
BCU-Net: Bridging ConvNeXt and U-Net for medical image segmentation
inferredcites →https://doi.org/10.1016/j.knosys.2019.04.025openalex-extracted
DUNet: A deformable network for retinal vessel segmentation
inferredcites →https://doi.org/10.1016/j.media.2021.101971openalex-extracted
Applications of deep learning in fundus images: A review
inferredcites →https://doi.org/10.1016/j.neucom.2020.06.143openalex-extracted
A refined equilibrium generative adversarial network for retinal vessel segmentation
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01350openalex-extracted
Coordinate Attention for Efficient Mobile Network Design
inferredcites →https://doi.org/10.1109/lgrs.2018.2802944openalex-extracted
Road Extraction by Deep Residual U-Net
inferredcites →https://doi.org/10.1109/tmi.2004.825627openalex-extracted
Ridge-Based Vessel Segmentation in Color Images of the Retina
inferredcites →https://doi.org/10.1109/tmi.2007.898551openalex-extracted
Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification
inferredcites →https://doi.org/10.1109/tmi.2019.2959609openalex-extracted
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
inferredcites →https://doi.org/10.1109/wacv45572.2020.9093621openalex-extracted
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
inferredcites →https://doi.org/10.1117/12.535349openalex-extracted
Comparative study of retinal vessel segmentation methods on a new publicly available database
inferredcites →https://doi.org/10.1155/2022/9917691openalex-extracted
MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation
inferredcites →https://doi.org/10.1167/iovs.08-3018openalex-extracted
Measuring Retinal Vessel Tortuosity in 10-Year-Old Children: Validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) Program
inferredcites →https://doi.org/10.1186/1475-925x-9-14openalex-extracted
A novel method for blood vessel detection from retinal images
inferredcites →https://doi.org/10.3390/e22080811openalex-extracted
A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1810.07810openalex-extracted
LadderNet: Multi-path networks based on U-Net for medical image segmentation
inferredcites →https://doi.org/10.48550/arxiv.1909.11936openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2101.09864openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2102.04306openalex-extracted
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
inferredcites →https://doi.org/10.48550/arxiv.2105.05537openalex-extracted
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