CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation
Medical image segmentation requires high accuracy and robustness, yet practical commercial deployment also demands privacy preservation and computational efficiency. In this context, the U-Net architecture, which can be inherently decoupled into independent encoder and decoder components, serves as a natural commercial choice. However, pure Transformer-based variants like Swin-UNet often suffer from insufficient local detail capture and limited interpretability. In this paper, we propose a lightweight hybrid architecture built upon the Swin-UNet framework. Our model integrates a parallel CNN encoder to complement the shallow layer reasoning of Swin Transformers with local texture features...
| Authors | Bin Dong, Jinghong Chen |
|---|---|
| Primary category | eess.IV |
| Published | 23 August 2026 |
| Last updated at source | 23 August 2026 |
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