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A Multi-Scale Feature Fusion Method Based on U-Net for Retinal Vessel Segmentation
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/mp.12953openalex-snapshot
Retinal vascular segmentation using superpixel‐based line operator and its application to vascular topology estimation
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s11548-017-1619-0openalex-snapshot
Multi-level deep supervised networks for retinal vessel segmentation
inferredcites →https://doi.org/10.1007/s11760-017-1114-7openalex-snapshot
Contrast normalization steps for increased sensitivity of a retinal image segmentation method
inferredcites →https://doi.org/10.1016/j.amc.2018.07.057openalex-snapshot
Blood vessel segmentation in retinal fundus images using Gabor filters, fractional derivatives, and Expectation Maximization
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Blood vessel segmentation methodologies in retinal images – A survey
inferredcites →https://doi.org/10.1016/j.compbiomed.2019.103352openalex-snapshot
Multi-proportion channel ensemble model for retinal vessel segmentation
inferredcites →https://doi.org/10.1016/j.compmedimag.2009.09.006openalex-snapshot
Multi-scale retinal vessel segmentation using line tracking
inferredcites →https://doi.org/10.1016/j.ins.2017.08.050openalex-snapshot
Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network
inferredcites →https://doi.org/10.1016/j.media.2014.08.002openalex-snapshot
Trainable COSFIRE filters for vessel delineation with application to retinal images
inferredcites →https://doi.org/10.1016/j.neucom.2018.05.011openalex-snapshot
Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function
inferredcites →https://doi.org/10.1016/j.patcog.2011.01.007openalex-snapshot
Segmentation of retinal blood vessels using the radial projection and semi-supervised approach
inferredcites →https://doi.org/10.1016/j.patcog.2012.12.014openalex-snapshot
Retinal vessel segmentation using multiwavelet kernels and multiscale hierarchical decomposition
inferredcites →https://doi.org/10.1109/42.34715openalex-snapshot
Detection of blood vessels in retinal images using two-dimensional matched filters
inferredcites →https://doi.org/10.1109/42.845178openalex-snapshot
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
inferredcites →https://doi.org/10.1109/access.2018.2869858openalex-snapshot
Mapping Functions Driven Robust Retinal Vessel Segmentation via Training Patches
inferredcites →https://doi.org/10.1109/access.2019.2920616openalex-snapshot
Deep Learning Models for Retinal Blood Vessels Segmentation: A Review
inferredcites →https://doi.org/10.1109/access.2019.2922365openalex-snapshot
Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network
inferredcites →https://doi.org/10.1109/access.2019.2935138openalex-snapshot
A Fundus Retinal Vessels Segmentation Scheme Based on the Improved Deep Learning U-Net Model
inferredcites →https://doi.org/10.1109/access.2019.2945556openalex-snapshot
Micro-Vessel Image Segmentation Based on the AD-UNet Model
inferredcites →https://doi.org/10.1109/access.2019.2953259openalex-snapshot
An Improved Retinal Vessel Segmentation Framework Using Frangi Filter Coupled With the Probabilistic Patch Based Denoiser
inferredcites →https://doi.org/10.1109/cvpr.2015.7298594openalex-snapshot
Going deeper with convolutions
inferredcites →https://doi.org/10.1109/iccv.2015.164openalex-snapshot
Holistically-Nested Edge Detection
inferredcites →https://doi.org/10.1109/icpr.2008.4761762openalex-snapshot
Retinal vessel tree segmentation using a deformable contour model
inferredcites →https://doi.org/10.1109/isbi.2016.7493362openalex-snapshot
Retinal vessel segmentation via deep learning network and fully-connected conditional random fields
inferredcites →https://doi.org/10.1109/isbi.2017.7950512openalex-snapshot
A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation
inferredcites →https://doi.org/10.1109/jbhi.2014.2335617openalex-snapshot
Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Sub-Image Classification
inferredcites →https://doi.org/10.1109/tbme.2010.2097599openalex-snapshot
Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction
inferredcites →https://doi.org/10.1109/tbme.2012.2205687openalex-snapshot
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation
inferredcites →https://doi.org/10.1109/tbme.2016.2535311openalex-snapshot
A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images
inferredcites →https://doi.org/10.1109/tmi.2004.825627openalex-snapshot
Ridge-Based Vessel Segmentation in Color Images of the Retina
inferredcites →https://doi.org/10.1109/tmi.2006.879955openalex-snapshot
Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction
inferredcites →https://doi.org/10.1109/tmi.2006.879967openalex-snapshot
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification
inferredcites →https://doi.org/10.1109/tmi.2010.2064333openalex-snapshot
A New Supervised Method for Blood Vessel Segmentation in Retinal Images by Using Gray-Level and Moment Invariants-Based Features
inferredcites →https://doi.org/10.1109/tmi.2015.2457891openalex-snapshot
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images
inferredcites →https://doi.org/10.1109/tmi.2016.2546227openalex-snapshot
Segmenting Retinal Blood Vessels With Deep Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2003.1159954openalex-snapshot
Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images
inferredcites →https://doi.org/10.1109/tpami.2013.50openalex-snapshot
Representation Learning: A Review and New Perspectives
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1109/wiecon-ece.2018.8783182openalex-snapshot
Retinal Blood Vessel Segmentation Using Residual Block Incorporated U-Net Architecture and Fuzzy Inference System
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1155/2015/597475openalex-snapshot
Adaptive Thresholding Technique for Retinal Vessel Segmentation Based on GLCM-Energy Information
inferredcites →https://doi.org/10.1166/jmihi.2019.2733openalex-snapshot
Retinal Blood Vessel Segmentation with Improved Convolutional Neural Networks
inferredcites →https://doi.org/10.3389/fncom.2019.00044openalex-snapshot
Inception Modules Enhance Brain Tumor Segmentation
inferredcites →https://doi.org/10.3389/fpls.2019.01404openalex-snapshot
Learning Semantic Graphics Using Convolutional Encoder–Decoder Network for Autonomous Weeding in Paddy
inferredcites →https://doi.org/10.3390/app9194043openalex-snapshot
MFCSNet: Multi-Scale Deep Features Fusion and Cost-Sensitive Loss Function Based Segmentation Network for Remote Sensing Images
inferredcites →https://doi.org/10.3390/e21040423openalex-snapshot
Optimizing Deep CNN Architectures for Face Liveness Detection
inferredcites →https://doi.org/10.3390/s19224949openalex-snapshot
A Multi-Scale Directional Line Detector for Retinal Vessel Segmentation
inferredcites →https://doi.org/10.3390/sym11091112openalex-snapshot
Automatic Retinal Blood Vessel Segmentation Based on Fully Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1504.06375openalex-snapshot
Holistically-Nested Edge Detection
inferredcites →https://doi.org/10.48550/arxiv.1704.07502openalex-snapshot
A Labeling-Free Approach to Supervising Deep Neural Networks for Retinal Blood Vessel Segmentation
inferredcites →https://doi.org/10.57702/o9raffedopenalex-snapshot
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4676215openalex-extracted
Wfl-Vnet: Retinal Vessel Segmentation Method Using Whole-Process Feature Localization
inferred← citeshttps://doi.org/10.1002/mp.14944openalex-snapshot
Densely connected U‐Net retinal vessel segmentation algorithm based on multi‐scale feature convolution extraction
inferred← citeshttps://doi.org/10.1007/978-3-031-01984-5_9openalex-snapshot
Parameter Selection of Contrast Limited Adaptive Histogram Equalization Using Multi-Objective Flower Pollination Algorithm
inferred← citeshttps://doi.org/10.1007/978-981-19-0840-8_43openalex-snapshot
Exploring Unet Architecture for Semantic Segmentation of the Brain MRI Scans
inferred← citeshttps://doi.org/10.1007/978-981-19-1253-5_20openalex-snapshot
MLFF: Multiple Low-Level Features Fusion Model for Retinal Vessel Segmentation
inferred← citeshttps://doi.org/10.1007/978-981-96-9949-0_7openalex-snapshot
DCA-UNet: A Diffusion-Based Insulator Defect Image Generation Model with Dual Cross-Attention Mechanisms
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inferred← citeshttps://doi.org/10.1007/s10278-025-01545-zopenalex-snapshot
AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation
inferred← citeshttps://doi.org/10.1007/s11042-024-18696-wopenalex-snapshot
Retinal blood vessel segmentation using a deep learning method based on modified U-NET model
inferred← citeshttps://doi.org/10.1007/s11042-024-18757-0openalex-snapshot
An improved method for retinal vessel segmentation in U-Net
inferred← citeshttps://doi.org/10.1007/s11220-026-00804-9openalex-snapshot
InceptionNeXt Unet Retinal Vessel Segmentation Algorithm with Channel Attention Mechanism
inferred← citeshttps://doi.org/10.1007/s11760-025-03917-yopenalex-snapshot
WFL-Vnet: retinal vessel segmentation method using whole-process feature localization
inferred← citeshttps://doi.org/10.1016/j.aej.2023.10.040openalex-snapshot
Segmentation of diabetic retinopathy images using deep feature fused residual with U-Net
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.107003openalex-snapshot
BINet: Bio-inspired network for retinal vessel segmentation
inferred← citeshttps://doi.org/10.1016/j.cmpb.2021.106081openalex-snapshot
A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105651openalex-snapshot
CRAUNet: A cascaded residual attention U-Net for retinal vessel segmentation
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MF2ResU-Net: a multi-feature fusion deep learning architecture for retinal blood vessel segmentation
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Assessment of retinal blood vessel segmentation using U-Net model: A deep learning approach
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Leukocyte segmentation based on DenseREU-Net
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Early Glaucoma Detection by Using Style Transfer to Predict Retinal Nerve Fiber Layer Thickness Distribution on the Fundus Photograph
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Optimization of table tennis target detection algorithm guided by multi-scale feature fusion of deep learning
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Ensemble-based eye disease detection system utilizing fundus and vascular structures
inferred← citeshttps://doi.org/10.1088/1361-6560/ac1c4copenalex-snapshot
UNet retinal blood vessel segmentation algorithm based on improved pyramid pooling method and attention mechanism
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Multi-level spatial-temporal and attentional information deep fusion network for retinal vessel segmentation
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TDCAU-Net: retinal vessel segmentation using transformer dilated convolutional attention-based U-Net method
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Typical Ground Object Recognition in Desert Areas Based on DYDCNet: A Case Study in the Circum-Tarim Region, Xinjiang, China
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A Novel Approach With Enhanced Accuracy and Efficiency for Image Segmentation
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Analysis of Security Measures on the Internet of Things based Applications
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Retinal Vessel Segmentation Algorithm Based on Improved Gaussian Matched Filter
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B-COSFIRE and Background Normalisation for Efficient Segmentation of Retinal Vessels
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MRDP-UNet: A Multi-Resolution Deep Learning Framework with Directional Gabor Feature Enhancement and Adaptive Parallel Attention for Retinal Vessel Segmentation
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Automatic Retinal Vessel Segmentation Based on an Improved U-Net Approach
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BVU-Net: A U-Net Modification by VGG-Batch Normalization for Retinal Blood Vessel Segmentation
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Retinal blood vessel segmentation using a deep learning method based on modified U-NET model
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Retinal blood vessel segmentation using a deep learning method based on modified U-NET model
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Peer Review #1 of "DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.871v0.1/reviews/2openalex-snapshot
Peer Review #2 of "DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel (v0.1)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.871v0.2/reviews/1openalex-snapshot
Peer Review #1 of "DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel (v0.2)"
inferred← citeshttps://doi.org/10.7287/peerj-cs.871v0.2/reviews/2openalex-snapshot
Peer Review #2 of "DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel (v0.2)"
inferred← citeshttps://doi.org/10.7717/peerj-cs.871openalex-snapshot
DBFU-Net: Double branch fusion U-Net with hard example weighting train strategy to segment retinal vessel
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