Papers › Dual Cross-Attention for Medical Image Segmentation
Dual Cross-Attention for Medical Image Segmentation
Gorkem Can Ates, Prasoon Mohan, Emrah Celik
We propose Dual Cross-Attention (DCA), a simple yet effective attention module that is able to enhance skip-connections in U-Net-based architectures for medical image segmentation. DCA addresses the semantic gap between encoder and decoder features by sequentially capturing channel and spatial dependencies across multi-scale encoder features. First, the Channel Cross-Attention (CCA) extracts global channel-wise dependencies by utilizing cross-attention across channel tokens of multi-scale encoder features. Then, the Spatial Cross-Attention (SCA) module performs cross-attention to capture spatial dependencies across spatial tokens. Finally, these fine-grained encoder features are up-sampled and connected to their corresponding decoder parts to form the skip-connection scheme. Our proposed DCA module can be integrated into any encoder-decoder architecture with skip-connections such as U-Net and its variants. We test our DCA module by integrating it into six U-Net-based architectures such as U-Net, V-Net, R2Unet, ResUnet++, DoubleUnet and MultiResUnet. Our DCA module shows Dice Score improvements up to 2.05% on GlaS, 2.74% on MoNuSeg, 1.37% on CVC-ClinicDB, 1.12% on Kvasir-Seg and 1.44% on Synapse datasets. Our codes are available at: https://github.com/gorkemcanates/Dual-Cross-Attention
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Medical Image Segmentation | CVC-ClinicDB | DoubleUnet-DCA | mIoU | 0.8347 | #41 of 48 | Archive leaderboard | report |
| Medical Image Segmentation | CVC-ClinicDB | DoubleUnet-DCA | mean Dice | 0.9086 | #41 of 48 | Archive leaderboard | report |
| Medical Image Segmentation | Kvasir-SEG | DoubleUnet-DCA | mIoU | 0.7434 | #51 of 58 | Archive leaderboard | report |
| Medical Image Segmentation | Kvasir-SEG | DoubleUnet-DCA | mean Dice | 0.8516 | #51 of 58 | Archive leaderboard | report |
| Medical Image Segmentation | MoNuSeg | DoubleUnet-DCA | IoU | 65.97 | #14 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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