Skin Lesion Segmentation on PH2 (test)
96.01DSCAlrabai et al. (2025)
Evaluation results
| Method | Links | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alrabai et al. (2025)Backbone=full-scale hybrid ViT, Explainability=No2026.06 | 96.01 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | |
| EAM-NetPara=4.6M, Flops=16.85G2025.12 | 95.15 | — | — | 90.88 | — | — | — | — | 96.99 | 96.58 | — | — | — | — | — | |
| PVT-CASCADEPara=35.27M, Flops=8.20G2025.12 | 95.03 | — | — | 90.31 | — | — | — | — | 96.58 | 94.48 | — | — | — | — | — | |
| EMCADPara=30.0M, Flops=6.36G2025.12 | 95 | — | — | 90.56 | — | — | — | — | 96.69 | 95.03 | — | — | — | — | — | |
| G-CASCADEPara=141.4M, Flops=30.42G2025.12 | 94.75 | — | — | 90.11 | — | — | — | — | 96.21 | 94.94 | — | — | — | — | — | |
| Parallel MERITPara=147.9M, Flops=33.43G2025.12 | 94.69 | — | — | 90.21 | — | — | — | — | 96.81 | 95.59 | — | — | — | — | — | |
| CPF-NetPara=30.65M, Flops=8.83G2025.12 | 94.52 | — | — | 89.91 | — | — | — | — | 96.72 | 95.56 | — | — | — | — | — | |
| CE-NetPara=29.0M, Flops=9.76G2025.12 | 94.36 | — | — | 89.62 | — | — | — | — | 96.68 | 94.54 | — | — | — | — | — | |
| DenseASPPPara=33.7M, Flops=107.74G2025.12 | 94.13 | — | — | 89.38 | — | — | — | — | 96.61 | 96.54 | — | — | — | — | — | |
| MambaLiteUNet2026.04 | 93.92 | — | — | 88.54 | — | — | — | — | 94.08 | — | 90.45 | 97.79 | — | — | — | |
| LB-UNet2026.04 | 93.12 | — | — | 87.12 | — | — | — | — | 93.41 | — | 88.12 | 98.83 | — | — | — | |
| ULVM-UNet2026.04 | 93.1 | — | — | 87.1 | — | — | — | — | 93.31 | — | 89.31 | 97.4 | — | — | — | |
| BCDU-NetPara=28.8M, Flops=360.18G2025.12 | 93.06 | — | — | 87.41 | — | — | — | — | 95.61 | 95.56 | — | — | — | — | — | |
| EGE-UNet2026.04 | 93.03 | — | — | 86.97 | — | — | — | — | 93.31 | — | 88.2 | 98.55 | — | — | — | |
| U-NetPara=32.9M, Flops=65.53G2025.12 | 92.62 | — | — | 87.07 | — | — | — | — | 95.57 | 93.32 | — | — | — | — | — | |
| MALUNet2026.04 | 92.47 | — | — | 85.99 | — | — | — | — | 92.76 | — | 87.75 | 97.9 | — | — | — | |
| BPC-Netlearning_paradigm=unsupervised2026.04 | 92.14 | — | — | 86.35 | — | — | — | — | 94.77 | — | 93.78 | 95.09 | — | — | — | |
| LightM-UNet2026.04 | 92.11 | — | — | 85.38 | — | — | — | — | 92.45 | — | 87.14 | 97.88 | — | — | — | |
| VM-UNet22026.04 | 92.07 | — | — | 85.3 | — | — | — | — | 92.47 | — | 86.43 | 98.65 | — | — | — | |
| UGDD-NetSpatial-Frequency (SF)=true, Uncertainty-aware (U)=true2026.05 | 92.07 | — | — | 85.93 | — | — | — | — | — | — | — | — | 11.07 | 4.26 | 11.24 | |
| VM-UNet2026.04 | 91.73 | — | — | 84.73 | — | — | — | — | 92.01 | — | 87.63 | 96.49 | — | — | — | |
| MADGNetTrain Dataset=ISIC20182024.05 | 91.3 | — | — | 84.6 | 88.4 | 76.2 | 92.8 | 5.1 | — | — | — | — | — | — | — | |
| TransFuse2026.04 | 91.03 | — | — | 83.54 | — | — | — | — | 91.43 | — | 85.98 | 97.01 | — | — | — | |
| UDELUncertainty-aware (U)=true2026.05 | 90.96 | — | — | 84.01 | — | — | — | — | — | — | — | — | 12.25 | 5.23 | 13.76 | |
| CDMT-UNetSpatial-Frequency (SF)=true2026.05 | 90.82 | — | — | 83.93 | — | — | — | — | — | — | — | — | 12.45 | 5.44 | 19.89 | |
| SFma-UNetSpatial-Frequency (SF)=true2026.05 | 90.79 | — | — | 83.88 | — | — | — | — | — | — | — | — | 12.65 | 5.43 | 17.67 | |
| CFFormer2026.05 | 90.71 | — | — | 83.76 | — | — | — | — | — | — | — | — | 14.95 | 6.3 | 19.73 | |
| M2SNetTrain Dataset=ISIC20182024.05 | 90.7 | — | — | 83.5 | 87.6 | 75.5 | 92 | 5.9 | — | — | — | — | — | — | — | |
| FRCUNetTrain Dataset=ISIC20182024.05 | 90.6 | — | — | 83.4 | 87.4 | 75.4 | 91.7 | 5.9 | — | — | — | — | — | — | — | |
| CENetTrain Dataset=ISIC20182024.05 | 90.5 | — | — | 83.3 | 87.3 | 75.1 | 91.5 | 6 | — | — | — | — | — | — | — | |
| MSRFNetTrain Dataset=ISIC20182024.05 | 90.5 | — | — | 83.5 | 87.5 | 75 | 91.4 | 6 | — | — | — | — | — | — | — | |
| UNeXt-S2026.04 | 90.41 | — | — | 82.51 | — | — | — | — | 90.73 | — | 86.37 | 95.21 | — | — | — | |
| DPMF-NetSpatial-Frequency (SF)=true2026.05 | 90.4 | — | — | 83.23 | — | — | — | — | — | — | — | — | 13.02 | 5.63 | 16.52 | |
| DEviSUUncertainty-aware (U)=true2026.05 | 90.36 | — | — | 83.15 | — | — | — | — | — | — | — | — | 14.07 | 6.22 | 14.17 | |
| SLEDlearning_paradigm=unsupervised2026.04 | 90.34 | — | — | 83.5 | — | — | — | — | 93 | — | 89.97 | 96.37 | — | — | — | |
| F2CAU-NetUncertainty-aware (U)=true2026.05 | 90.33 | — | — | 83.19 | — | — | — | — | — | — | — | — | 13.55 | 5.75 | 14.25 | |
| UNetTrain Dataset=ISIC20182024.05 | 90.3 | — | — | 83.5 | 88.4 | 74.8 | 90.8 | 6.9 | — | — | — | — | — | — | — | |
| SkinMambaSpatial-Frequency (SF)=true2026.05 | 90.29 | — | — | 82.87 | — | — | — | — | — | — | — | — | 12.73 | 5.52 | 17.32 | |
| C2SDG2026.04 | 90.25 | — | — | 82.23 | — | — | — | — | 90.68 | — | 85.2 | 96.3 | — | — | — | |
| DPGNetUncertainty-aware (U)=true2026.05 | 90.12 | — | — | 83.01 | — | — | — | — | — | — | — | — | 13.84 | 6.09 | 14.36 | |
| AttUNetTrain Dataset=ISIC20182024.05 | 89.9 | — | — | 82.6 | 87.3 | 74.8 | 90.8 | 6.7 | — | — | — | — | — | — | — | |
| BDFormer2026.05 | 89.89 | — | — | 82.56 | — | — | — | — | — | — | — | — | 13.44 | 5.72 | 20.29 | |
| DSU-Net2026.05 | 89.77 | — | — | 82.54 | — | — | — | — | — | — | — | — | 16.63 | 6.77 | 19.28 | |
| ASwin U-Net2026.04 | 89.75 | — | — | 81.41 | — | — | — | — | 90.45 | — | 82.63 | 98.47 | — | — | — | |
| WinGraphUNet2026.05 | 89.63 | — | — | 82.42 | — | — | — | — | — | — | — | — | 12.41 | 5.35 | 19.27 | |
| TransUNetTrain Dataset=ISIC20182024.05 | 89.5 | — | — | 82.1 | 86.9 | 74.3 | 90.3 | 6.7 | — | — | — | — | — | — | — | |
| MPBA-Net2026.05 | 89.39 | — | — | 81.91 | — | — | — | — | — | — | — | — | 13.56 | 5.92 | 18.48 | |
| AHF-U-NetUncertainty-aware (U)=true2026.05 | 89.3 | — | — | 81.74 | — | — | — | — | — | — | — | — | 14.08 | 6.15 | 14.6 | |
| DMA-Net2026.05 | 89.19 | — | — | 81.41 | — | — | — | — | — | — | — | — | 14.6 | 6.26 | 17.36 | |
| U-Net2026.04 | 89.09 | — | — | 80.33 | — | — | — | — | 89.79 | — | 82.4 | 97.35 | — | — | — | |
| DCSAUNetTrain Dataset=ISIC20182024.05 | 89 | — | — | 81.5 | 85.7 | 74 | 90.2 | 6.9 | — | — | — | — | — | — | — | |
| RPI-Netlearning_paradigm=unsupervised2026.04 | 88.91 | — | — | 81.98 | — | — | — | — | 93.25 | — | 93.66 | 94.6 | — | — | — | |
| USL-Netlearning_paradigm=unsupervised2026.04 | 88.9 | — | — | 80.11 | — | — | — | — | 92.43 | — | 93.62 | 93.07 | — | — | — | |
| SF-UNetSpatial-Frequency (SF)=true2026.05 | 88.58 | — | — | 80.84 | — | — | — | — | — | — | — | — | 13.33 | 5.74 | 15.64 | |
| VM-UNet2026.05 | 88.45 | — | — | 80.69 | — | — | — | — | — | — | — | — | 17.03 | 6.92 | 16.74 | |
| Swin-Unet2026.05 | 88.14 | — | — | 79.93 | — | — | — | — | — | — | — | — | 18.46 | 7.31 | 20.47 | |
| UNet++Train Dataset=ISIC20182024.05 | 88 | — | — | 80.1 | 85.7 | 73.2 | 89.2 | 7.9 | — | — | — | — | — | — | — | |
| U-Net2026.05 | 87.95 | — | — | 79.81 | — | — | — | — | — | — | — | — | 17.15 | 6.89 | 15.87 | |
| SGSCNlearning_paradigm=unsupervised2026.04 | 87.89 | — | — | 80.15 | — | — | — | — | 91.8 | — | 84.26 | 93.82 | — | — | — | |
| H-Net2026.05 | 87.73 | — | — | 79.67 | — | — | — | — | — | — | — | — | 17.52 | 7.06 | 15.45 | |
| HiFormerTrain Dataset=ISIC20182024.05 | 86.9 | — | — | 79.1 | 83.2 | 72.9 | 88.6 | 8 | — | — | — | — | — | — | — | |
| K-meanslearning_paradigm=unsupervised2026.04 | 86.14 | — | — | 77.78 | — | — | — | — | 91.64 | — | 83.82 | 95.03 | — | — | — | |
| UTNetV22026.04 | 85.85 | — | — | 75.21 | — | — | — | — | 87.27 | — | 76.28 | 98.53 | — | — | — | |
| SCR-Net2026.04 | 85.13 | — | — | 74.1 | — | — | — | — | 86.63 | — | 75.61 | 97.91 | — | — | — | |
| Sp. Merginglearning_paradigm=unsupervised2026.04 | 82.83 | — | — | 73.97 | — | — | — | — | 89.1 | — | 79.63 | 96 | — | — | — | |
| NCutlearning_paradigm=unsupervised2026.04 | 80.17 | — | — | 70.51 | — | — | — | — | 88.15 | — | 76.16 | 94.94 | — | — | — | |
| MedSAM (box)protocol=oracle-box, contextual_reference=prompt-based foundation-model transfer2026.04 | 78.42 | — | — | 68.66 | — | — | — | — | 88.93 | — | 69.5 | 98.56 | — | — | — | |
| A2S-v2learning_paradigm=unsupervised2026.04 | 78.21 | — | — | 67.15 | — | — | — | — | 85.66 | — | 87.56 | 91.37 | — | — | — | |
| SpecWRSClearning_paradigm=unsupervised2026.04 | 78.12 | — | — | 68.54 | — | — | — | — | 85.96 | — | 79.08 | 89.74 | — | — | — | |
| Saliency-CCElearning_paradigm=unsupervised2026.04 | 76.79 | — | — | 65.62 | — | — | — | — | 84.62 | — | 78.9 | 93.77 | — | — | — | |
| DRClearning_paradigm=unsupervised2026.04 | 72.41 | — | — | 59.76 | — | — | — | — | 82.63 | — | 69.18 | 97.65 | — | — | — | |
| IARS SegNetAbbreviation=SN+SC+RC+AM, Skip Connections=true, Residual Convolutions=true, Attention Mechanism=true2023.10 | — | 1.01 | 0.3 | — | — | — | — | — | — | — | — | — | — | — | — | |
| SegNetAbbreviation=SN2023.10 | — | 1.44 | 0.35 | — | — | — | — | — | — | — | — | — | — | — | — |