Medical Image Segmentation On Monuseg
Métriques
F1
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | F1 |
|---|---|
| hi-gmisnet-generalized-medical-image | 82.50 |
| medical-transformer-gated-axial-attention-for | 76.83 |
| lvit-language-meets-vision-transformer-in | 79.26 |
| cell-detection-with-star-convex-polygons | 84.6 |
| lvit-language-meets-vision-transformer-in | 77.01 |
| medical-transformer-gated-axial-attention-for | 79.56 |
| histoseg-quick-attention-with-multi-loss | 75.08 |
| lvit-language-meets-vision-transformer-in | 81.01 |
| lvit-language-meets-vision-transformer-in | 79.87 |
| lvit-language-meets-vision-transformer-in | 80.66 |
| dual-cross-attention-for-medical-image | - |
| medical-transformer-gated-axial-attention-for | 79.55 |
| masked-diffusion-as-self-supervised | 81.01 |