Colorectal Gland Segmentation On Crag
Métriques
Dice
F1-score
Hausdorff Distance (mm)
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Dice | F1-score | Hausdorff Distance (mm) |
|---|---|---|---|
| pseudo-label-guided-contrastive-learning-for | 0.892 | 0.881 | 119.5 |
| mild-net-minimal-information-loss-dilated | 0.883 | 0.869 | 146.2 |
| rotation-equivariant-vector-field-networks | 0.758 | 0.745 | 287.5 |
| learning-steerable-filters-for-rotation | 0.848 | 0.811 | 175.9 |
| u-net-convolutional-networks-for-biomedical | - | - | 199.5 |
| dense-steerable-filter-cnns-for-exploiting | 0.891 | 0.874 | 138.4 |
| learning-steerable-filters-for-rotation | 0.888 | 0.861 | 139.5 |
| learning-steerable-filters-for-rotation | 0.870 | 0.855 | 156.2 |
| rotation-equivariant-vector-field-networks | 0.782 | 0.776 | 251.9 |
| group-equivariant-convolutional-networks | 0.856 | 0.833 | 170.4 |
| roto-translation-equivariant-convolutional | 0.866 | 0.837 | 157.4 |
| rotation-equivariant-vector-field-networks | 0.721 | 0.711 | 318.9 |
| learning-steerable-filters-for-rotation | 0.869 | 0.837 | 164.8 |
| roto-translation-equivariant-convolutional | 0.834 | 0.818 | 192.2 |
| u-net-convolutional-networks-for-biomedical | 0.844 | - | 196.9 |