Low-dose CT Denoising on Mayo 2016 (test)
39.26Client #1 PSNRProFed
Evaluation results
| Method | Links | |||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ProFedArchitecture=Transformer2026.03 | 39.26 | — | — | — | 96.18 | 44.33 | 98.63 | 44.9 | 98.95 | 44.6 | 98.89 | 48.91 | 99.4 | 46.58 | 99.02 | 45.3 | 98.97 | 44.76 | 98.84 | 44.83 | 98.61 | |
| SCAN-PhysFedArchitecture=Transformer2026.03 | 38.55 | — | — | — | 94.06 | 41.74 | 96.2 | 45.52 | 98.52 | 42.48 | 96.55 | 49.5 | 99.39 | 45.32 | 97.93 | 43.35 | 96.73 | 42.71 | 96.79 | 43.65 | 97.03 | |
| FedFDDArchitecture=Transformer2026.03 | 37.35 | — | — | — | 96.2 | 39.67 | 95.18 | 44.31 | 98.26 | 39 | 95.21 | 47.89 | 99.2 | 43.81 | 98.06 | 42.17 | 97.4 | 40.5 | 95.49 | 41.84 | 96.89 | |
| ProFedBackbone Architecture=CNN-based2026.03 | 36.63 | — | — | — | 97.18 | 41.79 | 97.88 | 42.43 | 98.31 | 42.29 | 98.25 | 47.87 | 98.91 | 43.82 | 98.54 | 42.94 | 98.36 | 42.69 | 98.19 | 42.56 | 98.23 | |
| FedFDDBackbone Architecture=CNN-based2026.03 | 36.18 | — | — | — | 93.67 | 38.43 | 94.19 | 39.72 | 96.47 | 38.51 | 95.3 | 45.56 | 98.73 | 42.72 | 97.61 | 40.74 | 96.01 | 38.54 | 94.39 | 40.05 | 95.79 | |
| FedKDArchitecture=Transformer2026.03 | 35.92 | — | — | — | 89.93 | 38.7 | 91.21 | 44.26 | 98.13 | 42.3 | 95.99 | 45.93 | 98.75 | 43.57 | 97.41 | 42.54 | 96.22 | 43.51 | 96.23 | 42.09 | 95.48 | |
| FedPerArchitecture=Transformer2026.03 | 35.37 | — | — | — | 93.18 | 39.03 | 93.55 | 42.75 | 97.13 | 40.88 | 96.2 | 46.55 | 98.83 | 42.87 | 96.68 | 41.87 | 96.7 | 40.79 | 94.78 | 41.26 | 95.82 | |
| FedMRIArchitecture=Transformer2026.03 | 34.6 | — | — | — | 93.67 | 38.04 | 94.55 | 42.94 | 97.44 | 39.34 | 95.78 | 46.79 | 98.91 | 42.82 | 97.3 | 40.81 | 95.32 | 40.85 | 95.7 | 40.78 | 96.01 | |
| HyperFedArchitecture=Transformer2026.03 | 34.6 | — | — | — | 87.59 | 38.62 | 91.08 | 44.13 | 97.61 | 42.25 | 96.24 | 45.92 | 98.59 | 43.71 | 97.37 | 42.44 | 96.66 | 43.56 | 96.63 | 41.9 | 95.22 | |
| SCAN-PhysFedBackbone Architecture=CNN-based2026.03 | 34.49 | — | — | — | 96.4 | 39.21 | 96.09 | 40.45 | 97.93 | 40.51 | 97.61 | 47.81 | 99.14 | 44.08 | 98.51 | 42.54 | 97.86 | 40.02 | 97.37 | 41.14 | 97.62 | |
| FedProxArchitecture=Transformer2026.03 | 34.32 | — | — | — | 87.01 | 37.74 | 88.89 | 43.73 | 97.95 | 41.12 | 93.92 | 45.39 | 98.61 | 42.99 | 97.08 | 41.38 | 94.31 | 42.1 | 94.55 | 41.1 | 94.04 | |
| FedAvgArchitecture=Transformer2026.03 | 34.31 | — | — | — | 87.88 | 38.06 | 90.54 | 43.51 | 97.56 | 41.05 | 95.43 | 45.24 | 98.47 | 43.15 | 97.09 | 41.45 | 95.95 | 42.88 | 96.16 | 41.21 | 94.89 | |
| MOONArchitecture=Transformer2026.03 | 34.28 | — | — | — | 93.2 | 38.09 | 91.17 | 43.64 | 97.77 | 41.09 | 95.66 | 45.25 | 98.43 | 42.82 | 96.91 | 41.42 | 95.97 | 42.2 | 96.18 | 41.1 | 95.66 | |
| HyperFedBackbone Architecture=CNN-based2026.03 | 33.74 | — | — | — | 88.57 | 34.83 | 85.05 | 38.87 | 92.78 | 35.44 | 85.4 | 42.94 | 97.48 | 39.15 | 92.96 | 34.99 | 85.19 | 38.12 | 91.29 | 37.26 | 89.84 | |
| FedDGBackbone Architecture=CNN-based2026.03 | 33.37 | — | — | — | 81.4 | 34.01 | 82.39 | 33.56 | 76.37 | 34.43 | 81.49 | 34.06 | 76.7 | 33.97 | 80.17 | 34.75 | 80.87 | 34.25 | 79.23 | 34.05 | 79.83 | |
| FedMRIBackbone Architecture=CNN-based2026.03 | 33.28 | — | — | — | 83.43 | 34.51 | 85.81 | 38.62 | 92.47 | 35.86 | 87.21 | 41.49 | 96.27 | 38.22 | 91.32 | 35.87 | 86.34 | 38.09 | 91.94 | 36.99 | 89.35 | |
| FedPerBackbone Architecture=CNN-based2026.03 | 33.23 | — | — | — | 88.2 | 35.06 | 84.89 | 38.79 | 92.7 | 35.58 | 86.12 | 41.76 | 96.97 | 36.85 | 89.96 | 35.69 | 86.3 | 38.48 | 91.69 | 36.93 | 89.6 | |
| FedBNBackbone Architecture=CNN-based2026.03 | 32.55 | — | — | — | 84.59 | 34.76 | 84.15 | 38.18 | 90.3 | 34.77 | 83.3 | 41.24 | 96.11 | 38.66 | 91.19 | 34.74 | 83.35 | 37.89 | 90.35 | 36.6 | 87.92 | |
| MOONBackbone Architecture=CNN-based2026.03 | 32.5 | — | — | — | 75.61 | 35.55 | 85.41 | 35.72 | 80.3 | 35.69 | 82.1 | 35.38 | 78.47 | 36.31 | 84.35 | 35.7 | 81.17 | 35.62 | 80.4 | 35.31 | 80.98 | |
| FedAvgBackbone Architecture=CNN-based2026.03 | 29.99 | — | — | — | 82.2 | 32.82 | 78.23 | 34.68 | 86.99 | 32.3 | 80.63 | 36.87 | 92.06 | 37.36 | 89.02 | 32.73 | 80.66 | 35.46 | 87.65 | 34.03 | 84.7 | |
| FedNovaBackbone Architecture=CNN-based2026.03 | 29.95 | — | — | — | 82.29 | 32.57 | 80.57 | 37.77 | 92.16 | 32.09 | 78.98 | 34.42 | 88.69 | 36.05 | 89.57 | 32.81 | 81.41 | 35.89 | 88.71 | 33.94 | 85.3 | |
| FedProxBackbone Architecture=CNN-based2026.03 | 29.94 | — | — | — | 83.12 | 32.69 | 75.46 | 36.95 | 91.58 | 32.32 | 80.91 | 38.95 | 95.34 | 37.58 | 88.13 | 32.94 | 81.62 | 35.86 | 88.65 | 34.65 | 85.6 | |
| FedKDBackbone Architecture=CNN-based2026.03 | 29.74 | — | — | — | 83.11 | 32.17 | 71.65 | 36.65 | 90.93 | 32.1 | 79.19 | 40.58 | 96.71 | 37.07 | 82.55 | 32.88 | 81.3 | 36.28 | 89.08 | 34.68 | 84.31 | |
| FedNovaArchitecture=Transformer2026.03 | 29.55 | — | — | — | 78.44 | 31.38 | 68.13 | 37.07 | 82.47 | 31.79 | 73.45 | 40.61 | 88.8 | 35.46 | 79.77 | 32.38 | 73.66 | 35.77 | 82.22 | 34.25 | 78.37 | |
| ASCON2023.07 | — | 44.48 | 0.6 | 97.49 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Blind2Unblind2024.03 | — | 35.84 | — | 81.15 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| BM3D2024.03 | — | 35.5 | — | 86.95 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| CNCL2023.07 | — | 43.06 | 0.71 | 96.68 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| CUT2024.03 | — | 37.96 | — | 89.93 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| CycleGAN2024.03 | — | 37.68 | — | 89.22 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| DIP2024.03 | — | 37.25 | — | 85.94 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| DU-GAN2023.07 | — | 43.06 | 0.71 | 96.34 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| EDCNN2023.07 | — | 43.14 | 0.7 | 96.45 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| ESAU-Net2023.07 | — | 44.38 | 0.61 | 97.47 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Neighbor2Neighbor2024.03 | — | 35.27 | — | 86.79 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| Noise2Sim2024.03 | — | 38.51 | — | 90.15 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| RED-CNN2023.07 | — | 44.23 | 0.62 | 97.34 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| U-Net2023.07 | — | 44.13 | 0.64 | 97.38 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| WGAN-VGG2023.07 | — | 42.49 | 0.76 | 96.16 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| WIA-LD2ND2024.03 | — | 38.15 | — | 90 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| ZS-N2N2024.03 | — | 38.1 | — | 87.64 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |