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EAA-Net: Rethinking the Autoencoder Architecture with Intra-class Features for Medical Image Segmentation
However, segmentation networks pay too much attention to the main visual difference between foreground and background, and ignores the detailed edge information, which leads to a reduction in the accuracy of edge segmentation.
TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation
These differences will affect the results of the automatic segmentation methods.
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