Medical Image Segmentation on BTCV (test)
89.07Dice ScoreResenc U-Net
Evaluation results
| Method | Links | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Resenc U-Net#models=5-fold ensemble, pre-training=MultiTalent, multiple datasets=true2023.03 | 89.07 | 15.01 | — | — | — | — | — | — | — | — | |
| MultiTalent Resenc U-Net#models=5-fold ensemble, multiple datasets=true2023.03 | 88.91 | 14.68 | — | — | — | — | — | — | — | — | |
| MultiTalent Resenc U-Net#models=single model, multiple datasets=true2023.03 | 88.82 | 16.35 | — | — | — | — | — | — | — | — | |
| nnU-Net (cascaded & fullres)#models=2 models, each 5-fold ensemble2023.03 | 88.1 | 17.26 | — | — | — | — | — | — | — | — | |
| DoDNet (pretrained)#models=single model, multiple datasets=true2023.03 | 86.44 | 15.62 | — | — | — | — | — | — | — | — | |
| Universal Model#models=single model, multiple datasets=true2023.03 | 86.13 | — | — | — | — | — | — | — | — | — | |
| PaNN#models=single model, multiple datasets=true2023.03 | 84.97 | 18.47 | — | — | — | — | — | — | — | — | |
| Single-task model (Swin-UNETR)#param=30.7M, Step=Step 12024.06 | 82.7 | — | — | — | — | — | — | — | — | — | |
| LwF#param=37.5M, Step=Step 1, backbone=Swin-UNETR2024.06 | 82.7 | — | — | — | — | — | — | — | — | — | |
| ILT#param=37.5M, Step=Step 1, backbone=Swin-UNETR2024.06 | 82.7 | — | — | — | — | — | — | — | — | — | |
| PLOP#param=37.5M, Step=Step 1, backbone=Swin-UNETR2024.06 | 82.7 | — | — | — | — | — | — | — | — | — | |
| Low-Rank MoE#param=2.3M, Step=Step 12024.06 | 82.6 | — | — | — | — | — | — | — | — | — | |
| SwinUNETR#models=single model2023.03 | 82.06 | — | — | — | — | — | — | — | — | — | |
| Single-task model#param=62.1M, Step=Step 12024.06 | 81.9 | — | — | — | — | — | — | — | — | — | |
| CLAMTS#param=37.5M, Step=Step 1, backbone=Swin-UNETR2024.06 | 81.9 | — | — | — | — | — | — | — | — | — | |
| UNETR#models=single model2023.03 | 81.43 | — | — | — | — | — | — | — | — | — | |
| Low-Rank MoE#param=2.3M, Step=Step 22024.06 | 80.6 | — | — | — | — | — | — | — | — | — | |
| CLAMTS#param=37.5M, Step=Step 2, backbone=Swin-UNETR2024.06 | 78.5 | — | — | — | — | — | — | — | — | — | |
| PLOP#param=37.5M, Step=Step 2, backbone=Swin-UNETR2024.06 | 78 | — | — | — | — | — | — | — | — | — | |
| ILT#param=37.5M, Step=Step 2, backbone=Swin-UNETR2024.06 | 77.8 | — | — | — | — | — | — | — | — | — | |
| LwF#param=37.5M, Step=Step 2, backbone=Swin-UNETR2024.06 | 76.2 | — | — | — | — | — | — | — | — | — | |
| GPAFormerParameter (M)=1.81, FLOPs (GFLOPs)=24.05, Inf. Time (S)=0.99282026.04 | 75.7 | — | — | — | — | — | — | — | — | — | |
| Swin-UNETRParameter (M)=62.19, FLOPs (GFLOPs)=329.20, Inf. Time (S)=2.20612026.04 | 75.05 | — | — | — | — | — | — | — | — | — | |
| nnUNetParameter (M)=30.71, FLOPs (GFLOPs)=1297.62, Inf. Time (S)=18.10432026.04 | 73.79 | — | — | — | — | — | — | — | — | — | |
| UNETR++Parameter (M)=29.87, FLOPs (GFLOPs)=58.33, Inf. Time (S)=1.17822026.04 | 73.77 | — | — | — | — | — | — | — | — | — | |
| nnFormerParameter (M)=149.32, FLOPs (GFLOPs)=273.41, Inf. Time (S)=2.36272026.04 | 73.45 | — | — | — | — | — | — | — | — | — | |
| SegFormer3DParameter (M)=4.50, FLOPs (GFLOPs)=5.01, Inf. Time (S)=0.16442026.04 | 70.32 | — | — | — | — | — | — | — | — | — | |
| UNETRParameter (M)=92.78, FLOPs (GFLOPs)=82.70, Inf. Time (S)=0.48172026.04 | 69.82 | — | — | — | — | — | — | — | — | — | |
| CVIR2026.05 | — | — | 59.6 | 82.9 | 72.4 | 71.6 | 112.96 | 77.88 | 102.56 | 97.8 | |
| CycleMix2026.05 | — | — | 73.8 | 92.2 | 82.3 | 82.7 | 89.09 | 29.35 | 32.52 | 50.32 | |
| FullSup-nnUNetsupervision=fully supervised2026.05 | — | — | 83.8 | 96.8 | 87.5 | 89.3 | 19.46 | 4.24 | 10.25 | 11.31 | |
| FullSupUNetsupervision=fully supervised2026.05 | — | — | 82.5 | 96.1 | 86.7 | 88.4 | 23.72 | 4.31 | 10.81 | 12.95 | |
| GatedCRF2026.05 | — | — | 60.8 | 91.9 | 82.5 | 78.3 | 109.1 | 44.97 | 35.49 | 63.21 | |
| HELPNet2026.05 | — | — | 79.6 | 94 | 84.2 | 85.9 | 36.48 | 18.97 | 21.31 | 25.59 | |
| nnPU2026.05 | — | — | 55.7 | 91.5 | 80.9 | 76 | 128.65 | 34.76 | 75.59 | 79.67 | |
| PCE2026.05 | — | — | 38.4 | 85.5 | 72.5 | 65.5 | 213.93 | 68.61 | 100.68 | 127.74 | |
| ScribFormer2026.05 | — | — | 75.2 | 93 | 82.1 | 83.4 | 62.54 | 21.97 | 33.38 | 39.3 | |
| ShapePU2026.05 | — | — | 74.5 | 92.6 | 81.9 | 83 | 76.09 | 25.29 | 48.43 | 49.94 | |
| TIP252026.05 | — | — | 78 | 93.2 | 83.7 | 85 | 49.88 | 20.15 | 22.82 | 30.95 | |
| WSL42026.05 | — | — | 58.4 | 92 | 81.8 | 77.4 | 115.58 | 31.5 | 49.64 | 65.67 | |
| ZScribbleSeg2026.05 | — | — | 78.4 | 94.7 | 83.9 | 85.6 | 38.39 | 12.17 | 17.72 | 22.76 |