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UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace plain U-Net skip connections with CCT and CCA (ablation) | U-Net · GlaS | Dice: 85.45 -> 89.84 | Helped |
| replace plain U-Net skip connections with CCT and CCA (ablation) | U-Net · MoNuSeg | Dice: 76.45 -> 79.87 | Helped |
| add CCT for multi-scale encoder feature fusion (ablation) | U-Net · GlaS | Dice: 85.45 -> 89.09 | Helped |
| add CCT for multi-scale encoder feature fusion (ablation) | U-Net · MoNuSeg | Dice: 76.45 -> 79.31 | Helped |
| add CCA for decoder-feature fusion (ablation) | U-Net · GlaS | Dice: 85.45 -> 87.09 | Helped |
| add CCA for decoder-feature fusion (ablation) | U-Net · MoNuSeg | Dice: 76.45 -> 76.84 | Helped |
| remove CCA (ablation) | UCTransNet · Synapse | Dice: 78.23 -> 78.99 | Hurt |
| remove CCA (ablation) | UCTransNet · Synapse | HD: 26.75 -> 30.29 | Hurt |
| pre-train U-Net before training UCTransNet | UCTransNet · Synapse | Dice: 78.23 -> 75.54 | Hurt |
| pre-train U-Net before training UCTransNet | UCTransNet · Synapse | HD: 26.75 -> 38.97 | Hurt |
| use channel-wise skip fusion instead of the original U-Net connections | U-Net · GlaS | Dice: 85.45 -> 90.18$\pm$0.71$^\ast$ | Helped |
| use channel-wise skip fusion instead of the original U-Net connections | U-Net · MoNuSeg | Dice: 76.45$\pm$2.62 -> 79.08$\pm$0.67 | Helped |
Closest papers by meaning
- Rethinking U-Net: Task-Adaptive Mixture of Skip Connections for Enhanced Medical Image Segmentation (AAAI 2025 · similarity 0.90)
- Upping the Game: How 2D U-Net Skip Connections Flip 3D Segmentation (NeurIPS 2024 · similarity 0.89)
- nnWNet: Rethinking the Use of Transformers in Biomedical Image Segmentation and Calling for a Unified Evaluation Benchmark (CVPR 2025 · similarity 0.88)
- FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks (ICML 2025 · similarity 0.86)
- Universal Domain Adaptation for Semantic Segmentation (CVPR 2025 · similarity 0.85)
- Toward Real Ultra Image Segmentation: Leveraging Surrounding Context to Cultivate General Segmentation Model (NeurIPS 2024 · similarity 0.85)
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