Papers › DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation
Qing Xu, Zhicheng Ma, Na He, Wenting Duan
Deep learning architecture with convolutional neural network (CNN) achieves outstanding success in the field of computer vision. Where U-Net, an encoder-decoder architecture structured by CNN, makes a great breakthrough in biomedical image segmentation and has been applied in a wide range of practical scenarios. However, the equal design of every downsampling layer in the encoder part and simply stacked convolutions do not allow U-Net to extract sufficient information of features from different depths. The increasing complexity of medical images brings new challenges to the existing methods. In this paper, we propose a deeper and more compact split-attention u-shape network (DCSAU-Net), which efficiently utilises low-level and high-level semantic information based on two novel frameworks: primary feature conservation and compact split-attention block. We evaluate the proposed model on CVC-ClinicDB, 2018 Data Science Bowl, ISIC-2018 and SegPC-2021 datasets. As a result, DCSAU-Net displays better performance than other state-of-the-art (SOTA) methods in terms of the mean Intersection over Union (mIoU) and F1-socre. More significantly, the proposed model demonstrates excellent segmentation performance on challenging images. The code for our work and more technical details can be found at https://github.com/xq141839/DCSAU-Net.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Lesion Segmentation | ISIC 2018 Task 1 | DCSAU-Net | mIoU | 0.8301 | #1 of 1 | Archive leaderboard | report |
| Medical Image Segmentation | 2018 Data Science Bowl | DCSAU-Net | Recall | 0.9240 | #10 of 10 | Archive leaderboard | report |
| Medical Image Segmentation | 2018 Data Science Bowl | DCSAU-Net | mIoU | 0.8501 | #10 of 10 | Archive leaderboard | report |
| Medical Image Segmentation | ISIC 2018 | DCSAU-Net | DSC | 90.35 | #2 of 2 | Archive leaderboard | report |
| Medical Image Segmentation | ISIC2018 | U2netme | Accuracy | 0.94216 | #2 of 3 | Archive leaderboard | report |
| Medical Image Segmentation | ISIC2018 | U2netme | Precision | 0.89502 | #2 of 3 | Archive leaderboard | report |
| Medical Image Segmentation | ISIC2018 | U2netme | Test F1-Score | 0.90604 | #2 of 3 | Archive leaderboard | report |
| Medical Image Segmentation | ISIC2018 | U2netme | mean Dice | 0.905 | #2 of 3 | Archive leaderboard | report |
| Medical Image Segmentation | SegPC-2021 | DCSAU-Net | mIoU | 0.8048 | #1 of 1 | 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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