Medical Image Segmentation on BUSI, ISIC-2017, ISIC-2018, Kvasir-SEG, GLAS
88DSCMed-URWKV†
Evaluation results
| Method | Links | ||
|---|---|---|---|
| Med-URWKV†Type=RWKV, Source=Ours, Params=24.41 M2025.06 | 88 | 80.96 | |
| Med-URWKV-TType=RWKV, Source=Ours, Params=14.33 M2025.06 | 86.74 | 79.18 | |
| Med-URWKV-SType=RWKV, Source=Ours, Params=53.07 M2025.06 | 86.7 | 79.38 | |
| Swin-UMambaType=Mamba, Source=MICCAI’24, Params=55.06 M2025.06 | 86.2 | 78.45 | |
| RWKV-UNetType=RWKV, Source=Arxiv’25, Params=17.40 M2025.06 | 85.47 | 77.86 | |
| PVT-EMCAD-b1Type=CNN, Source=CVPR’24, Params=15.41 M2025.06 | 84.56 | 76.26 | |
| Zig-RiRType=RWKV, Source=TMI’25, Params=24.58 M2025.06 | 84.51 | 75.66 | |
| UCTransNetType=ViT, Source=AAAI’22, Params=66.24 M2025.06 | 84.29 | 76.39 | |
| TransUNetType=ViT, Source=Arxiv’21, Params=92.23 M2025.06 | 84.2 | 75.95 | |
| ACC-UnetType=CNN, Source=MICCAI’23, Params=16.77 M2025.06 | 83.96 | 76.03 | |
| UNeXtType=CNN, Source=MICCAI’22, Params=1.47 M2025.06 | 83.38 | 75.13 | |
| Attention-UNetType=CNN, Source=MIDL’18, Params=34.88 M2025.06 | 81.85 | 73.58 | |
| MGFuseSegType=CNN, Source=BIBM’23, Params=22.27 M2025.06 | 81.78 | 73.42 | |
| UNet++Type=CNN, Source=TMI’19, Params=47.19 M2025.06 | 81.49 | 72.97 | |
| H-VmnetType=Mamba, Source=Neurocomputing’25, Params=6.44 M2025.06 | 81.18 | 71.83 | |
| UNetType=CNN, Source=MICCAI’15, Params=24.89 M2025.06 | 81.11 | 72.18 | |
| MISSFormerType=ViT, Source=TMI’23, Params=35.45 M2025.06 | 79.97 | 70.65 | |
| VM-UNetType=Mamba, Source=Arxiv’24, Params=34.62 M2025.06 | 78.83 | 70.23 | |
| Swin-UnetType=ViT, Source=ECCV’22, Params=27.15 M2025.06 | 69.57 | 58.61 | |
| HFE-RWKVType=RWKV, Source=ICASSP’25, Params=49.67 M2025.06 | 67.81 | 56.39 |