Medical Image Segmentation on LiTS
72.14Dice ScoreInvCoSS
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| InvCoSSParadigm=Continual Self-supervised Learning, Access=Data-free2025.12 | 72.14 | 30.36 | — | — | |
| MedCoSSParadigm=Continual Self-supervised Learning, Access=Data-replay2025.12 | 72.01 | 36.5 | — | — | |
| CTParadigm=Static Pre-training Baseline2025.12 | 71.98 | 33.79 | — | — | |
| Joint SSLParadigm=Static Pre-training Baseline, Decoder=Shared2025.12 | 70.59 | 38.24 | — | — | |
| Joint SSLParadigm=Static Pre-training Baseline, Decoder=Separate2025.12 | 69.6 | 44.17 | — | — | |
| ERParadigm=Continual Self-supervised Learning, Access=Data-replay2025.12 | 69.53 | 41.86 | — | — | |
| MRIParadigm=Static Pre-training Baseline2025.12 | 68.54 | 43.79 | — | — | |
| CaSSLeParadigm=Continual Self-supervised Learning, Access=Data-free2025.12 | 67.04 | 47.17 | — | — | |
| EWCParadigm=Continual Self-supervised Learning, Access=Data-free2025.12 | 64.59 | 53.66 | — | — | |
| X-rayParadigm=Static Pre-training Baseline2025.12 | 62.96 | 55.79 | — | — | |
| PackNetParadigm=Continual Self-supervised Learning, Access=Data-free2025.12 | 61.89 | 62.12 | — | — | |
| Path.Paradigm=Static Pre-training Baseline2025.12 | 61.78 | 57.97 | — | — | |
| TFSParadigm=Static Pre-training Baseline2025.12 | 60.77 | 67.4 | — | — | |
| ReportParadigm=Static Pre-training Baseline2025.12 | 59.71 | 63.41 | — | — | |
| 3D RepUX-Net2023.03 | 0.949 | — | — | — | |
| 3D UX-NetKernel size=k=72023.03 | 0.939 | — | — | — | |
| DeferredSegExperts=1, Expert=true2026.04 | 0.9388 | — | 89.64 | 93.81 | |
| nn-UNet2023.03 | 0.935 | — | — | — | |
| RepOptimizer2023.03 | 0.934 | — | — | — | |
| SwinUNETR2023.03 | 0.933 | — | — | — | |
| 3D UX-NetKernel size=k=212023.03 | 0.929 | — | — | — | |
| nnFormer2023.03 | 0.927 | — | — | — | |
| TransBTS2023.03 | 0.926 | — | — | — | |
| UNETR2023.03 | 0.92 | — | — | — | |
| DeferredSegExperts=1, System=true2026.04 | 0.9101 | — | 85.81 | 88.99 | |
| DeferredSegExperts=3, Expert=true2026.04 | 0.8351 | — | 78.52 | 95.65 | |
| DeferredSegExperts=3, System=true2026.04 | 0.8182 | — | 74.59 | 84.39 | |
| CENet2026.04 | 0.7535 | — | 64.91 | 76.38 | |
| DeferredSegExperts=3, Model=true2026.04 | 0.6852 | — | 63.42 | 80.04 | |
| DeferredSegExperts=1, Model=true2026.04 | 0.6734 | — | 64.03 | 73 | |
| nnUNet v22026.04 | 0.6256 | — | 52.72 | 84.99 | |
| MedSAM2026.04 | 0.5962 | — | 47.83 | 76.18 | |
| MedSAMFine-tuned=true2026.04 | 0.592 | — | 47.55 | 75.97 |