用不确定性引导注意力融合，提升医疗图像分割的少样本性能
Uncertainty-Guided Cross Attention Ensemble Mean Teacher for Semi-supervised Medical Image Segmentation
- 通过交叉注意力集成与不确定性正则化，增强模型间差异性
- 仅用10%标注数据即逼近全监督效果，多中心前列腺/心脏MRI表现最优
- 适合医疗影像少样本场景，对标注成本敏感的研究者实用
本文提出一种新框架UG-CEMT，用于半监督医疗图像分割并达到当前最佳性能。该方法结合了协同训练与知识蒸馏的优点，基于受视觉变压器启发的交叉注意力集成均值教师框架（CEMT），引入不确定性引导的一致性正则化与强调不确定性的锐度感知最小化。UG-CEMT通过促进子网络间的高差异性，在保持一致网络架构和任务设定的前提下，显著提升半监督性能。实验表明，在多中心前列腺MRI与心脏MRI数据集上，其在差异性、域泛化能力及分割精度方面均优于均值教师和交叉伪监督等现有方法。仅使用10%标注数据，其性能已接近全监督方法，充分证明了对未标注数据的有效利用能力。代码已开源。
原文摘要 · Abstract (English)
This work proposes a novel framework, Uncertainty-Guided Cross Attention Ensemble Mean Teacher (UG-CEMT), for achieving state-of-the-art performance in semi-supervised medical image segmentation. UG-CEMT leverages the strengths of co-training and knowledge distillation by combining a Cross-attention Ensemble Mean Teacher framework (CEMT) inspired by Vision Transformers (ViT) with uncertainty-guided consistency regularization and Sharpness-Aware Minimization emphasizing uncertainty. UG-CEMT improves semi-supervised performance while maintaining a consistent network architecture and task setting by fostering high disparity between sub-networks. Experiments demonstrate significant advantages over existing methods like Mean Teacher and Cross-pseudo Supervision in terms of disparity, domain generalization, and medical image segmentation performance. UG-CEMT achieves state-of-the-art results on multi-center prostate MRI and cardiac MRI datasets, where object segmentation is particularly challenging. Our results show that using only 10\% labeled data, UG-CEMT approaches the performance of fully supervised methods, demonstrating its effectiveness in exploiting unlabeled data for robust medical image segmentation. The code is publicly available at \url{https://github.com/Meghnak13/UG-CEMT}
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