Retinal Vessel Segmentation On Stare
Métriques
F1 score
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | F1 score | Paper Title | Repository |
|---|---|---|---|
| Residual U-Net | 0.8388 | Road Extraction by Deep Residual U-Net | - |
| R2U-Net | 0.8475 | Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation | - |
| U-Net | 0.8373 | U-Net: Convolutional Networks for Biomedical Image Segmentation | - |
| FSG-Net | 0.8510 | Full-scale Representation Guided Network for Retinal Vessel Segmentation | - |
| RV-GAN | 0.8323 | RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network | - |
| U-Net ASPP | - | Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks | - |
| DUNet | 0.8143 | DUNet: A deformable network for retinal vessel segmentation | - |
| VGN | 0.8429 | Deep Vessel Segmentation By Learning Graphical Connectivity | - |
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