UniSemAlign：基于基础编码器的文本-原型对齐用于半监督病理分割
UniSemAlign: Text-Prototype Alignment with a Foundation Encoder for Semi-Supervised Histopathology Segmentation
AI总结：
UniSemAlign通过引入显式类别结构提升视觉分割，利用共享嵌入空间中的原型和文本对齐分支，减少类别模糊性并稳定伪标签优化，实验表明其在有限标注下显著优于现有半监督方法。
摘要：
在计算病理学中，半监督语义分割因像素级标注稀缺和伪标签不可靠而具有挑战性。我们提出UniSemAlign，一种双模态语义对齐框架，通过在像素级学习中注入显式类别结构来增强视觉分割。该框架基于病理预训练的Transformer编码器，引入互补的原型级和文本级对齐分支，在共享嵌入空间中提供结构化指导，减少类别模糊性并稳定伪标签优化。对齐的表示与视觉预测融合，为未标注的病理图像生成更可靠的监督。该框架通过监督分割、跨视图一致性及跨模态对齐目标进行端到端训练。在GlaS和CRAG数据集上的广泛实验表明，UniSemAlign在有限监督下显著优于最近的半监督基线，仅使用10%标注数据时在GlaS上Dice提升达2.6%，在CRAG上达8.6%，在20%监督下也取得显著提升。代码可在https://github.com/thailevann/UniSemAlign获取。
英文摘要：
Semi-supervised semantic segmentation in computational pathology remains challenging due to scarce pixel-level annotations and unreliable pseudo-label supervision. We propose UniSemAlign, a dual-modal semantic alignment framework that enhances visual segmentation by injecting explicit class-level structure into pixel-wise learning. Built upon a pathology-pretrained Transformer encoder, UniSemAlign introduces complementary prototype-level and text-level alignment branches in a shared embedding space, providing structured guidance that reduces class ambiguity and stabilizes pseudo-label refinement. The aligned representations are fused with visual predictions to generate more reliable supervision for unlabeled histopathology images. The framework is trained end-to-end with supervised segmentation, cross-view consistency, and cross-modal alignment objectives. Extensive experiments on the GlaS and CRAG datasets demonstrate that UniSemAlign substantially outperforms recent semi-supervised baselines under limited supervision, achieving Dice improvements of up to 2.6% on GlaS and 8.6% on CRAG with only 10% labeled data, and strong improvements at 20% supervision. Code is available at: https://github.com/thailevann/UniSemAlign
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。
