Retinal Vessel Segmentation On Chase_Db1
Metriken
AUC
Acc
F1 score
MCC
Sensitivity
mIOU
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | AUC | Acc | F1 score | MCC | Sensitivity | mIOU |
|---|---|---|---|---|---|---|
| full-scale-representation-guided-network-for | 0.9937 | 0.9751 | 0.8101 | 0.7989 | 0.8599 | 0.8268 |
| iternet-retinal-image-segmentation-utilizing | 0.9851 | - | 0.8073 | - | - | - |
| g-cascade-efficient-cascaded-graph | - | - | 0.8267 | - | 0.8493 | 0.7050 |
| laddernet-multi-path-networks-based-on-u-net | 0.9839 | - | 0.8031 | - | - | - |
| resolution-aware-design-of-atrous-rates-for | - | - | - | - | - | 0.8959 |
| sa-unet-spatial-attention-u-net-for-retinal | 0.9905 | - | 0.8153 | - | - | - |
| road-extraction-by-deep-residual-u-net | 0.9779 | - | 0.7800 | - | - | - |
| deep-vessel-segmentation-by-learning | 0.9830 | - | 0.8034 | - | - | - |
| dunet-a-deformable-network-for-retinal-vessel | 0.9804 | - | 0.7883 | - | - | - |
| recurrent-residual-convolutional-neural | 0.9815 | - | 0.7928 | - | - | - |
| rv-gan-retinal-vessel-segmentation-from | 0.9914 | - | 0.8957 | - | 0.8199 | 0.9705 |
| study-group-learning-improving-retinal-vessel | 0.9920 | - | 0.8271 | - | 0.8690 | - |
| u-net-convolutional-networks-for-biomedical | 0.9772 | - | - | - | - | - |
| g-cascade-efficient-cascaded-graph | - | - | 0.8251 | - | 0.8584 | 0.7024 |
| full-resolution-network-and-dual-threshold | 0.9913 | - | 0.8151 | - | 0.8798 | - |