CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation

• URL: http://arxiv.org/abs/2608.22281v1
• Date: Sun, 23 Aug 2026 08:19:55 GMT
• Title: CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation
• Abstract summary: We propose a lightweight hybrid architecture built upon the Swin-UNet framework.<n>Our model integrates a parallel CNN encoder to complement the shallow layer reasoning of Swin Transformers with local texture features.<n>Our approach achieves state-of-the-art competitive Dice scores and Hausdorff distances, offering an accurate, efficient, and interpretable solution for clinical deployment.
• Score: 6.540920253738164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. To bridge the semantic gap and enhance fine-grained spatial detail recovery, we design an asymmetric feature fusion strategy and introduce cross-layer skip (XSkip) connections that explicitly propagate shallow CNN features into the decoder. We further incorporate novel loss functions and an auxiliary supervision head (Aux-Head) to strengthen training stability, boundary delineation, and intermediate feature interpretability. Extensive experiments on the Synapse multi-organ segmentation dataset demonstrate that our approach achieves state-of-the-art competitive Dice scores and Hausdorff distances, offering an accurate, efficient, and interpretable solution for clinical deployment.

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