Table 8 Comparison of medical image segmentation baseline models on LiTS dataset.

From: Edge-guided multi-scale adaptive feature fusion network for liver tumor segmentation

Network

Dice

VOE

RVD

UNet6

0.6352

0.4643

− 0.1903

Att-UNet37

0.6474

0.4494

− 0.0329

TransUNet38

0.6710

0.4267

− 0.1232

TransNetR39

0.6123

0.4891

− 0.3343

PVTformer12

0.6957

0.4164

− 0.2326

UNeXt40

0.6554

0.4430

− 0.1625

Brau_Net +  + 41

0.6986

0.3957

− 0.1324

Swin Transformer42

0.7060

0.3967

− 0.1296

MAEG-Net(ours)

0.7184

0.3864

− 0.1238

  1. Significant values are in bold.
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