Enhancing Retinal Vascular Structure Segmentation in Images With a Novel Design Two-Path Interactive Fusion Module Model | ✓ Link | 0.9931 | | | | | | | | | | Swin-Res-Net | 2024-03-03 |
Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation | ✓ Link | 0.9889 | 0.8316 | 0.9705 | | 0.8356 | | | | | | FR-UNet | 2022-07-05 |
Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels | ✓ Link | 0.9886 | 0.8316 | | | 0.8380 | | | | | | Study Group Learning | 2021-03-05 |
SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation | ✓ Link | 0.9864 | 0.8263 | 0.9698 | | | | | | | | SA-UNet | 2020-04-07 |
Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation | ✓ Link | 0.9861 | | | | | | 0.8079 | 0.9701 | 0.7154 | 0.8347 | DEFFA-Unet | 2025-06-02 |
Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation | ✓ Link | 0.9855 | | 0.9712 | | | | | | | | U-Net | 2021-05-19 |
DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning | | 0.9846 | 0.8193 | 0.8082 | | 0.8307 | 0.9803 | | | | | DA-Net | 2024-03-07 |
Full-scale Representation Guided Network for Retinal Vessel Segmentation | ✓ Link | 0.9823 | 0.8322 | 0.9704 | 0.8406 | 0.8420 | | 0.8173 | | | | FSG-Net | 2025-01-31 |
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks | ✓ Link | 0.9816 | 0.8205 | | | | | | | | | IterNet | 2019-12-12 |
Deep Vessel Segmentation By Learning Graphical Connectivity | ✓ Link | 0.9802 | 0.8263 | | | | | | | | | VGN | 2018-06-06 |
DUNet: A deformable network for retinal vessel segmentation | | 0.9802 | 0.8237 | | | | | | | | | DUNet | 2018-11-03 |
LadderNet: Multi-path networks based on U-Net for medical image segmentation | ✓ Link | 0.9793 | 0.8202 | | | | | | | | | LadderNet | 2018-10-17 |
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions | ✓ Link | 0.9789 | 0.8224 | | | | | | | | | BCDU-Net (d=3) | 2019-08-31 |
Road Extraction by Deep Residual U-Net | ✓ Link | 0.9779 | 0.8149 | | | | | | | | | Residual U-Net | 2017-11-29 |
CE-Net: Context Encoder Network for 2D Medical Image Segmentation | ✓ Link | 0.9779 | | 0.9545 | | | | | | | | CE-Net | 2019-03-07 |
U-Net: Convolutional Networks for Biomedical Image Segmentation | ✓ Link | 0.9755 | 0.8142 | | | | | | | | | U-Net | 2015-05-18 |
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation | ✓ Link | | 0.8290 | 0.9707 | 0.7081 | 0.8281 | 0.9844 | | | | | MERIT-GCASCADE | 2023-10-24 |
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy | ✓ Link | | 0.8245 | | | | | | | | | ConvMixer | 2023-03-31 |
Deep Learning Architectures for Diagnosis of Diabetic Retinopathy | ✓ Link | | 0.8215 | | | | | | | | | ConvMixer-Light | 2023-03-31 |
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation | ✓ Link | | 0.8210 | 0.9689 | 0.6970 | 0.83 | 0.9822 | | | | | PVT-GCASCADE | 2023-10-24 |
Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images | ✓ Link | | 0.75 | 0.9593 | | 0.7119 | 0.9832 | | | | | DR_2021 | 2022-07-09 |
ET-Net: A Generic Edge-aTtention Guidance Network for Medical Image Segmentation | ✓ Link | | | 0.956 | 0.7744 | | | | | | | ET-Net | 2019-07-25 |