Binary Medical Image Segmentation on Data Science Bowl 2018 (test)
92.74DICEPVT-EMCAD-B2
Evaluation Results
| Method | Links | |
|---|---|---|
| PVT-EMCAD-B2#Params=26.76M, #FLOPs=5.6G, Resolution=256x2562024.05 | 92.74 | |
| PVT-EMCAD-B0#Params=3.92M, #FLOPs=0.84G, Resolution=256x2562024.05 | 92.46 | |
| PVT-CASCADE#Params=34.12M, #FLOPs=7.62G, Resolution=256x2562024.05 | 92.35 | |
| UNet#Params=24.53M, #FLOPs=65.53G, Resolution=256x2562024.05 | 92.23 | |
| AttnUNet#Params=34.88M, #FLOPs=66.64G, Resolution=256x2562024.05 | 92.22 | |
| DeepLabv3+#Params=39.76M, #FLOPs=14.92G, Resolution=256x2562024.05 | 92.14 | |
| TransUNet#Params=105.32M, #FLOPs=38.52G, Resolution=256x2562024.05 | 92.04 | |
| SSFormer-L#Params=66.22M, #FLOPs=17.28G, Resolution=256x2562024.05 | 92.03 | |
| UNet++#Params=9.16M, #FLOPs=34.65G, Resolution=256x2562024.05 | 91.97 | |
| SwinUNet#Params=27.17M, #FLOPs=6.2G, Resolution=224x2242024.05 | 91.03 | |
| TransFuse#Params=143.74M, #FLOPs=82.71G, Resolution=256x2562024.05 | 90.85 | |
| PolypPVT#Params=25.11M, #FLOPs=5.30G, Resolution=256x2562024.05 | 90.69 | |
| PraNet#Params=32.55M, #FLOPs=6.93G, Resolution=256x2562024.05 | 89.89 | |
| CaraNet#Params=46.64M, #FLOPs=11.48G, Resolution=256x2562024.05 | 89.15 | |
| UACANet-L#Params=69.16M, #FLOPs=31.51G, Resolution=256x2562024.05 | 88.86 | |
| UNeXt#Params=1.47M, #FLOPs=0.57G, Resolution=256x2562024.05 | 86.01 |