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ResearchFormerly WizWand

Medical Image Segmentation on AutoPET-II

390.91ThrG ScoreVeloxSeg
13.4316111.4308209.43307.4292Sep 26, 2025
Updated 4mo ago

Evaluation results

MethodLinks
VeloxSegType=CNN-Transformer, MP.=1.66, GF.=1.792025.09390.916.67
SegFormer-3DType=CNN-Transformer, MP.=4.50, GF.=5.112025.09364.243.31
U-KANType=CNN-KAN, MP.=7.06, GF.=22.902025.09187.060.82
Slim UNETRType=CNN-Transformer, MP.=1.77, GF.=3.832025.09178.3311.4
UNETR++Type=CNN-Transformer, MP.=19.97, GF.=57.932025.09161.150.67
UNETRType=CNN-Transformer, MP.=95.76, GF.=83.612025.09131.960.4
H-DenseFormerType=CNN-Transformer, MP.=3.64, GF.=71.912025.09123.350.44
UNetType=CNN, MP.=5.75, GF.=136.562025.09101.040.23
NestedformerType=CNN-Transformer, MP.=4.71, GF.=58.622025.0995.630.41
U-RWKVType=CNN-RWKV, MP.=1.44, GF.=20.682025.0982.09—
VNetType=CNN, MP.=45.60, GF.=322.222025.0958.990.14
SuperLightNetType=CNN-Transformer, MP.=2.75, GF.=19.422025.0955.480.27
HCMA-UNetType=CNN-Mamba, MP.=2.81, GF.=26.152025.0954.51—
A2FSegType=CNN, MP.=41.32, GF.=207.972025.0952.020.17
Swin UNETRType=CNN-Transformer, MP.=15.51, GF.=84.262025.0938.370.14
VSmTransType=CNN-Transformer, MP.=12.48, GF.=91.442025.0936.560.14
MedNeXt-SType=CNN, MP.=5.54, GF.=57.932025.0927.950.06