CiUNet：用于医学图像分割的混合Swin-CNN UNet
CiUNet: A Hybrid Swin-CNN UNet for Medical Image Segmentation
- Ciphowork GmbH（Ciphowork有限公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对医学图像分割的隐私、效率需求，提出基于Swin-UNet的混合架构，融合并行CNN编码器与XSkip连接等，在Synapse数据集上实现SOTA分割性能，为临床部署提供可行方案。
AI中文摘要：
医学图像分割需要高准确率与鲁棒性，而实际商业部署还要求隐私保护与计算效率。在此背景下，可天然解耦为独立编码器和解码器组件的U-Net架构成为自然的商业选择。然而，纯基于Transformer的变体如Swin-UNet常存在局部细节捕获不足、可解释性有限的问题。本文提出一种基于Swin-UNet框架的轻量混合架构，模型集成并行CNN编码器，用局部纹理特征补充Swin Transformer的浅层推理；为弥合语义差距、增强细粒度空间细节恢复，设计非对称特征融合策略，引入跨层跳跃（XSkip）连接，明确将浅层CNN特征传播至解码器；还加入新型损失函数与辅助监督头（Aux-Head），以提升训练稳定性、边界刻画能力与中间特征可解释性。在Synapse多器官分割数据集上的大量实验表明，该方法达到了具有竞争力的SOTA Dice分数与Hausdorff距离，为临床部署提供了准确、高效且可解释的解决方案。
英文摘要：
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.
