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UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer

AAAI 2022 · Detection and segmentation › CNN features · approach: Context modeling for semantic segmentation · 1,093 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace plain U-Net skip connections with CCT and CCA (ablation)U-Net · GlaSDice: 85.45 -> 89.84Helped
replace plain U-Net skip connections with CCT and CCA (ablation)U-Net · MoNuSegDice: 76.45 -> 79.87Helped
add CCT for multi-scale encoder feature fusion (ablation)U-Net · GlaSDice: 85.45 -> 89.09Helped
add CCT for multi-scale encoder feature fusion (ablation)U-Net · MoNuSegDice: 76.45 -> 79.31Helped
add CCA for decoder-feature fusion (ablation)U-Net · GlaSDice: 85.45 -> 87.09Helped
add CCA for decoder-feature fusion (ablation)U-Net · MoNuSegDice: 76.45 -> 76.84Helped
remove CCA (ablation)UCTransNet · SynapseDice: 78.23 -> 78.99Hurt
remove CCA (ablation)UCTransNet · SynapseHD: 26.75 -> 30.29Hurt
pre-train U-Net before training UCTransNetUCTransNet · SynapseDice: 78.23 -> 75.54Hurt
pre-train U-Net before training UCTransNetUCTransNet · SynapseHD: 26.75 -> 38.97Hurt
use channel-wise skip fusion instead of the original U-Net connectionsU-Net · GlaSDice: 85.45 -> 90.18$\pm$0.71$^\ast$Helped
use channel-wise skip fusion instead of the original U-Net connectionsU-Net · MoNuSegDice: 76.45$\pm$2.62 -> 79.08$\pm$0.67Helped

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