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

2026-08-23

AI Summary

本文提出CiUNet，一种结合Swin-Transformer和CNN的轻量级混合架构，通过引入新的特征融合策略和损失函数，解决医学图像分割中的准确性、鲁棒性和效率问题。

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.

Problem

• Medical Image Segmentation
• Privacy Preservation
• Computational Efficiency
• Local Detail Capture
• Interpretability

Innovation

• Hybrid Architecture
• Asymmetric Feature Fusion
• Cross-layer Skip Connections
• Auxiliary Supervision Head