Medical Image Segmentation on AutoPET-II
390.91ThrG ScoreVeloxSeg
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| VeloxSegType=CNN-Transformer, MP.=1.66, GF.=1.792025.09 | 390.91 | 6.67 | |
| SegFormer-3DType=CNN-Transformer, MP.=4.50, GF.=5.112025.09 | 364.24 | 3.31 | |
| U-KANType=CNN-KAN, MP.=7.06, GF.=22.902025.09 | 187.06 | 0.82 | |
| Slim UNETRType=CNN-Transformer, MP.=1.77, GF.=3.832025.09 | 178.33 | 11.4 | |
| UNETR++Type=CNN-Transformer, MP.=19.97, GF.=57.932025.09 | 161.15 | 0.67 | |
| UNETRType=CNN-Transformer, MP.=95.76, GF.=83.612025.09 | 131.96 | 0.4 | |
| H-DenseFormerType=CNN-Transformer, MP.=3.64, GF.=71.912025.09 | 123.35 | 0.44 | |
| UNetType=CNN, MP.=5.75, GF.=136.562025.09 | 101.04 | 0.23 | |
| NestedformerType=CNN-Transformer, MP.=4.71, GF.=58.622025.09 | 95.63 | 0.41 | |
| U-RWKVType=CNN-RWKV, MP.=1.44, GF.=20.682025.09 | 82.09 | — | |
| VNetType=CNN, MP.=45.60, GF.=322.222025.09 | 58.99 | 0.14 | |
| SuperLightNetType=CNN-Transformer, MP.=2.75, GF.=19.422025.09 | 55.48 | 0.27 | |
| HCMA-UNetType=CNN-Mamba, MP.=2.81, GF.=26.152025.09 | 54.51 | — | |
| A2FSegType=CNN, MP.=41.32, GF.=207.972025.09 | 52.02 | 0.17 | |
| Swin UNETRType=CNN-Transformer, MP.=15.51, GF.=84.262025.09 | 38.37 | 0.14 | |
| VSmTransType=CNN-Transformer, MP.=12.48, GF.=91.442025.09 | 36.56 | 0.14 | |
| MedNeXt-SType=CNN, MP.=5.54, GF.=57.932025.09 | 27.95 | 0.06 |