发表机构
Nanchang Hangkong University（南昌航空大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对半监督医学图像分割中特征可靠性被忽略、伪标签不可靠的问题，提出SAUF-Net，通过结构-外观分解等模块及不确定性反馈机制，在ISIC-2016等数据集上优于现有最优方法，低标注场景下表现更佳。
AI 中文摘要
半监督学习在降低医学图像分割的标注成本方面展现出巨大潜力，但现有多数方法主要通过预测级一致性利用未标注数据，却常忽略内部特征表示的可靠性。医学图像中，与目标相关的结构线索易与不稳定的外观变化纠缠，可能导致训练过程中伪标签不可靠及误差累积。为解决这些问题，本文提出SAUF-Net，即结合不确定性反馈的结构-外观表示学习网络，用于半监督医学图像分割。SAUF-Net采用结构-外观分解模块（SADM）将瓶颈特征分离为结构表示与外观表示；解耦引导模块（DGM）将这些表示注入解码过程，增强感知结构的分割效果。同时，辅助解码器生成分支特定预测以进行可靠性估计，以及用于外观交换一致性的融合预测。此外，本文引入外观交换一致性分支，鼓励结构表示在外观变化下保持稳定；还引入带有有效性头和不确定性头的可靠性图引导双头判别器，提供特征级不确定性反馈。在ISIC-2016和Kvasir-SEG数据集上的大量实验表明，SAUF-Net的性能优于当前最优的半监督方法，尤其在低标注设置下表现突出。
英文摘要
Semi-supervised learning has shown great potential for reducing annotation costs in medical image segmentation. However, most existing methods mainly exploit unlabeled data through prediction-level consistency, while the reliability of internal feature representations is often overlooked. In medical images, target-related structural cues are easily entangled with unstable appearance variations, which may lead to unreliable pseudo labels and error accumulation during training. To address these issues, we propose SAUF-Net, a Structure--Appearance Representation Learning with Uncertainty Feedback Network for semi-supervised medical image segmentation. SAUF-Net uses the Structure--Appearance Decomposition Module (SADM) to separate bottleneck features into structural and appearance representations. The Disentangled Guidance Module (DGM) injects these representations into the decoding process to enhance structure-aware segmentation. Meanwhile, the Auxiliary Decoder produces branch-specific predictions for reliability estimation and a fused prediction for appearance-swapped consistency. Furthermore, we introduce an Appearance-Swapped Consistency branch to encourage structural representations to remain stable under appearance variations. We also introduce a reliability-map-guided dual-head discriminator with a Validity Head and an Uncertainty Head to provide feature-level uncertainty feedback. Extensive experiments on ISIC-2016 and Kvasir-SEG demonstrate that SAUF-Net outperforms state-of-the-art semi-supervised methods, especially under low-label settings.
Journal ref14th International Conference on Image and Graphics (ICIG 2026)
