SHTA：用于半监督医学图像分割的语义硬令牌校正和中心对齐
SHTA: Semantic Hard Token Correction and Center Alignment for Semi-Supervised Medical Image Segmentation
AI总结：
针对半监督医学图像分割中硬区域语义一致性不足问题，提出SHTA方法，通过语义分配等技术细化语义表示，集成到多个框架并在相关数据集评估，能提升分割精度等性能，且仅增加训练开销。
摘要：
半监督医学图像分割的最新进展通过预测一致性、伪标签监督和硬区域监督取得了显著性能。然而，这些方法主要改善监督质量，而非明确强制硬区域学习表示中的语义一致性。因此，即使在更强的预测级监督下，语义分配不稳定的困难区域在训练中常无法建立语义一致的表示，限制了分割性能进一步提升。为解决此问题，我们提出SHTA（语义硬令牌校正和中心对齐），一个轻量级训练时语义表示分支。SHTA通过语义分配、硬令牌细化和语义中心对齐来细化中间语义表示，在保留原始预测路径且不增加推理成本的同时，提高硬区域的语义一致性。我们将SHTA集成到代表性半监督分割框架中，并在Synapse和AMOS数据集上进行评估。实验结果表明，SHTA在各框架中均带来一致的成对改进，在分割精度、弱器官恢复和语义模糊性降低方面尤其明显，且仅产生训练时开销。代码可在指定网址获取。
英文摘要：
Recent advances in semi-supervised medical image segmentation have achieved remarkable performance through prediction consistency, pseudo-label supervision, and hard-region supervision. However, these methods primarily improve supervision quality rather than explicitly enforcing semantic consistency in the learned representations of hard regions. Consequently, even under increasingly stronger prediction-level supervision, difficult regions exhibiting unstable semantic assignment often fail to establish semantically consistent representations during training, thereby limiting further segmentation improvement. To address this issue, we propose SHTA (Semantic Hard Token Correction and Center Alignment), a lightweight training-time semantic representation branch. Instead of introducing additional prediction supervision, SHTA refines intermediate semantic representations through Semantic Assignment, Hard Token Refinement, and Semantic Center Alignment, thereby improving semantic consistency in hard regions while preserving the original prediction pathway and introducing no additional inference cost. We integrate SHTA into representative semi-supervised segmentation frameworks, including GA-CPS, CPS, URPC, and MagicNet, and conduct evaluations on the Synapse and AMOS datasets. Experimental results demonstrate that SHTA delivers consistent paired improvements across frameworks, with especially clear gains in segmentation accuracy, weak-organ recovery, and semantic ambiguity reduction, while incurring only training-time overhead. The code is available at https://anonymous.4open.science/r/release_SHTA-42D5/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
