Medical Image Segmentation On Automatic
Métriques
Avg DSC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Avg DSC |
|---|---|
| transunet-transformers-make-strong-encoders | 87.57 |
| g-cascade-efficient-cascaded-graph | 92.23 |
| lhu-net-a-light-hybrid-u-net-for-cost | 92.65 |
| medical-image-segmentation-via-cascaded | 91.63 |
| g-cascade-efficient-cascaded-graph | 91.95 |
| swin-unet-unet-like-pure-transformer-for | 90.00 |
| nnformer-interleaved-transformer-for | 92.06 |
| the-fully-convolutional-transformer-for | 93.02 |
| ai-sam-automatic-and-interactive-segment | 93.89 |
| transunet-transformers-make-strong-encoders | 86.75 |
| ai-sam-automatic-and-interactive-segment | 92.06 |
| s2s2-semantic-stacking-for-robust-semantic | 90.4 |
| adaptive-t-vmf-dice-loss-for-multi-class | 94.26 |
| transunet-transformers-make-strong-encoders | 89.71 |
| mist-medical-image-segmentation-transformer | 92.56 |
| multi-scale-hierarchical-vision-transformer-1 | 92.32 |
| emcad-efficient-multi-scale-convolutional | 92.12 |
| segformer3d-an-efficient-transformer-for-3d | 90.96 |
| missformer-an-effective-medical-image | 87.9 |
| medical-image-segmentation-via-cascaded | 91.46 |