Cardiac Segmentation on ACDC
91.3RV ScorePVT-GDLA
Evaluation results
| Method | Links | |||||||
|---|---|---|---|---|---|---|---|---|
| PVT-GDLA2026.03 | 91.3 | 92.53 | 90.21 | 96.07 | — | — | — | |
| nnFormerParams(M)=150.52026.03 | 90.94 | 92.06 | 89.58 | 95.65 | — | — | — | |
| CENet2025.05 | 90.9 | — | 89.63 | 95.99 | 92.18 | — | — | |
| CENet2026.03 | 90.9 | 92.18 | 89.63 | 95.99 | — | — | — | |
| PVT-EMCAD-B2Backbone=PVTv2-B22024.05 | 90.65 | — | 89.68 | 96.02 | — | — | — | |
| PVT-EMCAD-B2Backbone=PVT-B22026.03 | 90.65 | 92.12 | 89.68 | 96.02 | — | — | — | |
| PVT-DiffAttn2026.03 | 90.64 | 92.2 | 89.9 | 96.06 | — | — | — | |
| PVT-SelfAttn2026.03 | 90.47 | 92.1 | 89.76 | 96.06 | — | — | — | |
| nnFormerPre-trained on ImageNet=true2022.03 | 90.27 | — | — | — | 91.62 | 89.23 | 95.36 | |
| TransCASCADE2024.05 | 90.25 | — | 89.14 | 95.5 | 91.63 | — | — | |
| Cascaded MERIT2024.05 | 90.23 | — | 89.53 | 95.8 | 91.85 | — | — | |
| PHTransPre-trained on ImageNet=false2022.03 | 90.13 | — | — | — | 91.79 | 89.48 | 95.76 | |
| nnU-NetPre-trained on ImageNet=false2022.03 | 90.11 | — | — | — | 91.36 | 88.75 | 95.23 | |
| SegMaFormerParams(M)=2.02026.03 | 90.06 | 91.11 | 89.1 | 94.14 | — | — | — | |
| PVT-CASCADE2024.05 | 89.97 | — | 88.9 | 95.5 | 91.46 | — | — | |
| PVT-LinearAttn2026.03 | 89.8 | 91.77 | 89.52 | 96 | — | — | — | |
| LeViT-UnetPre-trained on ImageNet=true2022.03 | 89.55 | — | — | — | 90.32 | 87.64 | 93.76 | |
| MISSFormerPre-trained on ImageNet=false2022.03 | 89.55 | — | — | — | 90.86 | 88.04 | 94.99 | |
| MISSFormer2024.05 | 89.55 | — | 88.04 | 94.99 | 90.86 | — | — | |
| MISSFormer2025.05 | 89.55 | — | 88.04 | 94.99 | 90.86 | — | — | |
| MISSFormer2026.03 | 89.55 | 90.86 | 88.04 | 94.99 | — | — | — | |
| LeViT-UNet-384Params(M)=52.172026.03 | 89.55 | 90.32 | 87.64 | 93.76 | — | — | — | |
| PVT-EMCAD-B0Backbone=PVTv2-B02024.05 | 89.37 | — | 88.99 | 95.65 | — | — | — | |
| U-KABS2026.02 | 88.97 | 90.53 | 87.8 | 94.76 | — | — | — | |
| TransUNet2021.02 | 88.86 | 89.71 | 84.53 | 95.73 | — | — | — | |
| TransUnet2021.05 | 88.86 | 89.71 | 84.53 | 95.73 | — | — | — | |
| TransUNetPre-trained on ImageNet=true2022.03 | 88.86 | — | — | — | 89.71 | 84.53 | 95.73 | |
| TransUNet2025.05 | 88.86 | — | 84.53 | 95.73 | 89.71 | — | — | |
| TransUNet2026.02 | 88.86 | 89.71 | 84.53 | 95.73 | — | — | — | |
| TransUNet2026.03 | 88.86 | 89.71 | 84.53 | 95.73 | — | — | — | |
| TransUNetParams(M)=96.072026.03 | 88.86 | 89.71 | 84.54 | 95.73 | — | — | — | |
| SwinUnet2021.05 | 88.55 | 90 | 85.62 | 95.83 | — | — | — | |
| Swin-UnetPre-trained on ImageNet=true2022.03 | 88.55 | — | — | — | 90 | 85.62 | 95.83 | |
| Swin-UNetBackbone=Swin Transformer2025.05 | 88.55 | — | 85.62 | 95.83 | 90 | — | — | |
| Swin-Unet2026.02 | 88.55 | 90 | 85.62 | 95.83 | — | — | — | |
| Swin-UNet2026.03 | 88.55 | 90 | 85.62 | 95.83 | — | — | — | |
| SwinUNetParams(M)=–2026.03 | 88.55 | 90 | 85.62 | 95.83 | — | — | — | |
| SegFormer3DParams(M)=4.52026.03 | 88.5 | 90.96 | 88.86 | 95.53 | — | — | — | |
| MCFormer2026.02 | 87.6 | 87.7 | 79.2 | 93.4 | — | — | — | |
| AttnUNetBackbone=ResNet-502021.02 | 87.58 | 86.75 | 79.2 | 93.47 | — | — | — | |
| R50 Att-UNetBackbone=ResNet-502021.05 | 87.58 | 86.75 | 79.2 | 93.47 | — | — | — | |
| R50+AttnUNetBackbone=ResNet-502024.05 | 87.58 | — | 79.2 | 93.47 | 86.75 | — | — | |
| R50+AttnUNetBackbone=ResNet-502025.05 | 87.58 | — | 79.2 | 93.47 | 86.75 | — | — | |
| R50+AttnUNetBackbone=ResNet-502026.03 | 87.58 | 86.75 | 79.2 | 93.47 | — | — | — | |
| U-NetBackbone=ResNet-502021.02 | 87.1 | 87.55 | 80.63 | 94.92 | — | — | — | |
| R50 U-NetBackbone=ResNet-502021.05 | 87.1 | 87.55 | 80.63 | 94.92 | — | — | — | |
| R50+UNetBackbone=ResNet-502024.05 | 87.1 | — | 80.63 | 94.92 | 87.55 | — | — | |
| R50+UNetBackbone=ResNet-502025.05 | 87.1 | — | 80.63 | 94.92 | 87.55 | — | — | |
| R50+UNetBackbone=ResNet-502026.03 | 87.1 | 87.55 | 80.63 | 94.92 | — | — | — | |
| TransUNet2024.05 | 86.67 | — | 87.27 | 95.18 | 89.71 | — | — | |
| MT-UNet2024.05 | 86.64 | — | 89.04 | 95.62 | 90.43 | — | — | |
| MT-UNet2025.05 | 86.64 | — | 89.04 | 95.62 | 90.43 | — | — | |
| MT-UNet2026.03 | 86.64 | 90.43 | 89.04 | 95.62 | — | — | — | |
| FCT2026.02 | 86.1 | 87.6 | 81.9 | 94.8 | — | — | — | |
| ViT-CUPBackbone=ResNet-502021.02 | 86.07 | 87.57 | 81.88 | 94.75 | — | — | — | |
| R50 ViTBackbone=ResNet-502021.05 | 86.07 | 87.57 | 81.88 | 94.75 | — | — | — | |
| R50+ViT+CUPBackbone=ResNet-50 + ViT2024.05 | 86.07 | — | 81.88 | 94.75 | 87.57 | — | — | |
| R50+ViT+CUPBackbone=ResNet-50 + ViT2025.05 | 86.07 | — | 81.88 | 94.75 | 87.57 | — | — | |
| R50+ViT+CUPBackbone=ResNet-502026.03 | 86.07 | 87.57 | 81.88 | 94.75 | — | — | — | |
| R50-VIT-CUPParams(M)=86.002026.03 | 86.07 | 87.57 | 81.88 | 94.75 | — | — | — | |
| SwinUNet2024.05 | 85.77 | — | 84.42 | 94.03 | 88.07 | — | — | |
| UNETR2026.02 | 85.29 | 88.61 | 86.52 | 94.02 | — | — | — | |
| UNETRParams(M)=92.492026.03 | 85.29 | 88.61 | 86.52 | 94.02 | — | — | — | |
| ViT-CUP2021.02 | 81.46 | 81.45 | 70.71 | 92.18 | — | — | — | |
| VIT+CUPBackbone=ViT2024.05 | 81.46 | — | 70.71 | 92.18 | 81.45 | — | — | |
| VIT+CUPBackbone=ViT2025.05 | 81.46 | — | 70.71 | 92.18 | 81.45 | — | — | |
| ViT-CUP2026.02 | 81.46 | 81.45 | 70.71 | 92.18 | — | — | — | |
| VIT-CUPParams(M)=86.002026.03 | 81.46 | 81.45 | 70.71 | 92.18 | — | — | — | |
| FedCANumber of annotated patients (N)=1, Federated fine-tuning=true2022.04 | — | — | — | — | 39.7 | — | — | |
| FedCANumber of annotated patients (N)=2, Federated fine-tuning=true2022.04 | — | — | — | — | 56.1 | — | — | |
| FedCANumber of annotated patients (N)=4, Federated fine-tuning=true2022.04 | — | — | — | — | 78.4 | — | — | |
| FedCANumber of annotated patients (N)=8, Federated fine-tuning=true2022.04 | — | — | — | — | 85.8 | — | — | |
| FedRotationNumber of annotated patients (N)=1, Federated fine-tuning=true2022.04 | — | — | — | — | 51.6 | — | — | |
| FedRotationNumber of annotated patients (N)=2, Federated fine-tuning=true2022.04 | — | — | — | — | 62.7 | — | — | |
| FedRotationNumber of annotated patients (N)=4, Federated fine-tuning=true2022.04 | — | — | — | — | 82.1 | — | — | |
| FedRotationNumber of annotated patients (N)=8, Federated fine-tuning=true2022.04 | — | — | — | — | 86.7 | — | — | |
| FedSimCLRNumber of annotated patients (N)=1, Federated fine-tuning=true2022.04 | — | — | — | — | 39.5 | — | — | |
| FedSimCLRNumber of annotated patients (N)=2, Federated fine-tuning=true2022.04 | — | — | — | — | 57.6 | — | — | |
| FedSimCLRNumber of annotated patients (N)=4, Federated fine-tuning=true2022.04 | — | — | — | — | 78.8 | — | — | |
| FedSimCLRNumber of annotated patients (N)=8, Federated fine-tuning=true2022.04 | — | — | — | — | 85.9 | — | — | |
| FedSwAVNumber of annotated patients (N)=1, Federated fine-tuning=true2022.04 | — | — | — | — | 50 | — | — | |
| FedSwAVNumber of annotated patients (N)=2, Federated fine-tuning=true2022.04 | — | — | — | — | 59.4 | — | — | |
| FedSwAVNumber of annotated patients (N)=4, Federated fine-tuning=true2022.04 | — | — | — | — | 81.5 | — | — | |
| FedSwAVNumber of annotated patients (N)=8, Federated fine-tuning=true2022.04 | — | — | — | — | 86.2 | — | — | |
| Local CLNumber of annotated patients (N)=1, Federated fine-tuning=true2022.04 | — | — | — | — | 47.3 | — | — | |
| Local CLNumber of annotated patients (N)=2, Federated fine-tuning=true2022.04 | — | — | — | — | 71.7 | — | — | |
| Local CLNumber of annotated patients (N)=4, Federated fine-tuning=true2022.04 | — | — | — | — | 78.4 | — | — | |
| Local CLNumber of annotated patients (N)=8, Federated fine-tuning=true2022.04 | — | — | — | — | 84.7 | — | — | |
| Primus-SParams(M)=23.92026.03 | — | 92.46 | — | — | — | — | — | |
| ProposedNumber of annotated patients (N)=1, Federated fine-tuning=true2022.04 | — | — | — | — | 64.6 | — | — | |
| ProposedNumber of annotated patients (N)=2, Federated fine-tuning=true2022.04 | — | — | — | — | 82.4 | — | — | |
| ProposedNumber of annotated patients (N)=4, Federated fine-tuning=true2022.04 | — | — | — | — | 87.1 | — | — | |
| ProposedNumber of annotated patients (N)=8, Federated fine-tuning=true2022.04 | — | — | — | — | 89.4 | — | — | |
| Random initNumber of annotated patients (N)=1, Federated fine-tuning=true2022.04 | — | — | — | — | 44.5 | — | — | |
| Random initNumber of annotated patients (N)=2, Federated fine-tuning=true2022.04 | — | — | — | — | 57.2 | — | — | |
| Random initNumber of annotated patients (N)=4, Federated fine-tuning=true2022.04 | — | — | — | — | 76.4 | — | — | |
| Random initNumber of annotated patients (N)=8, Federated fine-tuning=true2022.04 | — | — | — | — | 83.4 | — | — |