Medical Image Segmentation on BraTS-MET
64.41ET Dice ScoreTACO
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| TACOPre-training Method=Medical SSL, Backbone=Swin UNETR2026.05 | 64.41 | 69.52 | 70.6 | 68.17 | |
| SwinMM (Wang et al., 2023c)Pre-training Method=Medical SSL, Backbone=Swin UNETR2026.05 | 64.26 | 68.13 | 68.47 | 66.95 | |
| Swin UNETR (Tang et al., 2022)Pre-training Method=Medical SSL, Backbone=Swin UNETR2026.05 | 63.32 | 67.5 | 69.42 | 66.74 | |
| BrainMVP (Rui et al., 2025)Pre-training Method=Medical SSL, Backbone=UniFormer2026.05 | 62.42 | 67.63 | 70.45 | 66.83 | |
| S2DC (Pan et al., 2025)Pre-training Method=Medical SSL, Backbone=Swin UNETR2026.05 | 62.41 | 66.01 | 67.88 | 65.43 | |
| Swin UNETRPre-training Method=From Scratch, Backbone=Swin UNETR2026.05 | 62.1 | 66.07 | 68.31 | 65.5 | |
| VoCo (Wu et al., 2024)Pre-training Method=Medical SSL, Backbone=Swin UNETR2026.05 | 60.51 | 64.13 | 66.01 | 63.55 | |
| UniFormerPre-training Method=From Scratch, Backbone=UniFormer2026.05 | 59.49 | 62.42 | 64.77 | 62.23 | |
| M3AE (Liu et al., 2023)Pre-training Method=Medical SSL, Backbone=UNET3D2026.05 | 57.73 | 62.97 | 67.31 | 62.67 | |
| UNET3DPre-training Method=From Scratch, Backbone=UNET3D2026.05 | 53.47 | 58.24 | 63.97 | 58.56 | |
| UNETRPre-training Method=From Scratch, Backbone=UNETR2026.05 | 51.42 | 54.03 | 60.16 | 55.2 |