Medical Image Segmentation on CVC-ColonDB (test)
0.9231Dice ScorePVT-EMCAD-B2
Evaluation results
| Method | Links | ||
|---|---|---|---|
| PVT-EMCAD-B2#Params=26.76M, #FLOPs=5.6G, Resolution=256x2562024.05 | 0.9231 | — | |
| UMambaLoss=MASL2026.01 | 0.9223 | — | |
| SSFormer-L#Params=66.22M, #FLOPs=17.28G, Resolution=256x2562024.05 | 0.9211 | — | |
| DeepLabv3+#Params=39.76M, #FLOPs=14.92G, Resolution=256x2562024.05 | 0.9192 | — | |
| PVT-EMCAD-B0#Params=3.92M, #FLOPs=0.84G, Resolution=256x2562024.05 | 0.9171 | — | |
| DuckNetLoss=MASL2026.01 | 0.9167 | — | |
| TransUNet#Params=105.32M, #FLOPs=38.52G, Resolution=256x2562024.05 | 0.9163 | — | |
| PVT-CASCADE#Params=34.12M, #FLOPs=7.62G, Resolution=256x2562024.05 | 0.916 | — | |
| PolypPVT#Params=25.11M, #FLOPs=5.30G, Resolution=256x2562024.05 | 0.9153 | — | |
| CaraNet#Params=46.64M, #FLOPs=11.48G, Resolution=256x2562024.05 | 0.9119 | — | |
| SwinUNetLoss=MASL2026.01 | 0.9112 | — | |
| UACANet-L#Params=69.16M, #FLOPs=31.51G, Resolution=256x2562024.05 | 0.9102 | — | |
| PraNetLoss=MASL2026.01 | 0.9092 | — | |
| TransUNetLoss=MASL2026.01 | 0.9078 | — | |
| UMambaLoss=standard Dice loss2026.01 | 0.9078 | — | |
| S2M-NetLoss=MASL2026.01 | 0.9075 | — | |
| TransFuse#Params=143.74M, #FLOPs=82.71G, Resolution=256x2562024.05 | 0.9035 | — | |
| RAPUNetLoss=MASL2026.01 | 0.9023 | — | |
| DuckNetLoss=standard Dice loss2026.01 | 0.9012 | — | |
| U-Net++Loss=MASL2026.01 | 0.8989 | — | |
| S2M-NetLoss=standard Dice loss2026.01 | 0.8969 | — | |
| SwinUNetLoss=standard Dice loss2026.01 | 0.8967 | — | |
| SwinUNet#Params=27.17M, #FLOPs=6.2G, Resolution=224x2242024.05 | 0.8927 | — | |
| PraNetLoss=standard Dice loss2026.01 | 0.8923 | — | |
| TransUNetLoss=standard Dice loss2026.01 | 0.8923 | — | |
| PraNet#Params=32.55M, #FLOPs=6.93G, Resolution=256x2562024.05 | 0.8916 | — | |
| U-NetLoss=MASL2026.01 | 0.8856 | — | |
| RAPUNetLoss=standard Dice loss2026.01 | 0.8812 | — | |
| UNet++#Params=9.16M, #FLOPs=34.65G, Resolution=256x2562024.05 | 0.8788 | — | |
| U-Net++Loss=standard Dice loss2026.01 | 0.8767 | — | |
| AttnUNet#Params=34.88M, #FLOPs=66.64G, Resolution=256x2562024.05 | 0.8646 | — | |
| U-NetLoss=standard Dice loss2026.01 | 0.8634 | — | |
| UNet#Params=24.53M, #FLOPs=65.53G, Resolution=256x2562024.05 | 0.8395 | — | |
| UNeXt#Params=1.47M, #FLOPs=0.57G, Resolution=256x2562024.05 | 0.8384 | — | |
| SSformer-LTrain Set=Kvasir & CVC-ClinicDB2022.03 | 0.802 | 0.721 | |
| UACANet-STrain Set=Kvasir & CVC-ClinicDB2022.03 | 0.783 | 0.704 | |
| CaraNetTrain Set=Kvasir & CVC-ClinicDB2022.03 | 0.773 | 0.689 | |
| SSformer-STrain Set=Kvasir & CVC-ClinicDB2022.03 | 0.772 | 0.697 | |
| UACANet-LTrain Set=Kvasir & CVC-ClinicDB2022.03 | 0.751 | 0.678 | |
| ProMISe (Cross)Prompt Points=5, Trainable Parameters=45.6M, Training Images=1450/25942024.03 | 0.744 | 0.636 | |
| ProMISe (RN)Prompt Points=16, Trainable Parameters=23.0M, Training Images=1450/25942024.03 | 0.735 | 0.624 | |
| ProMISe (Cross)Prompt Points=16, Trainable Parameters=45.6M, Training Images=1450/25942024.03 | 0.732 | 0.626 | |
| PraNetTrain Set=Kvasir & CVC-ClinicDB2022.03 | 0.712 | 0.64 | |
| Med2DPrompt Points=5, Trainable Parameters=184.5M, Training Images=5838/79352024.03 | 0.686 | 0.576 | |
| Med2DPrompt Points=16, Trainable Parameters=184.5M, Training Images=5838/79352024.03 | 0.685 | 0.575 | |
| ProMISe (RN)Prompt Points=5, Trainable Parameters=23.0M, Training Images=1450/25942024.03 | 0.664 | 0.547 | |
| SAMPrompt Points=5, Trainable Parameters=-, Training Images=-2024.03 | 0.569 | 0.482 | |
| SAMPrompt Points=16, Trainable Parameters=-, Training Images=-2024.03 | 0.548 | 0.467 | |
| U-NetTrainable Parameters=-, Training Images=1450/25942024.03 | 0.512 | 0.444 | |
| ResUNet++Trainable Parameters=-, Training Images=1450/25942024.03 | 0.483 | 0.41 |