Abstract
While deep learning has demonstrated considerable promise in computer-aided diagnosis for pulmonary embolism (PE), practical deployment in Computed Tomography Pulmonary Angiography (CTPA) is often hindered by "domain shift" and the prohibitive cost of expert annotations. To address these challenges, an unsupervised domain adaptation (UDA) framework is proposed, utilizing a Transformer backbone and a Mean-Teacher architecture for cross-center semantic segmentation. The primary focus is placed on enhancing pseudo-label reliability by learning deep structural information within the feature space. Specifically, three modules are integrated and designed for this task: (1) a Prototype Alignment (PA) mechanism to reduce category-level distribution discrepancies; (2) Global and Local Contrastive Learning (GLCL) to capture both pixel-level topological relationships and global semantic representations; and (3) an Attention-based Auxiliary Local Prediction (AALP) module designed to reinforce sensitivity to small PE lesions by automatically extracting high-information slices from Transformer attention maps. Experimental validation conducted on cross-center datasets (FUMPE and CAD-PE) demonstrates significant performance gains. In the FUMPE -> CAD-PE task, the IoU increased from 0.1152 to 0.4153, while the CAD-PE -> FUMPE task saw an improvement from 0.1705 to 0.4302. Furthermore, the proposed method achieved a 69.9% Dice score in the CT -> MRI cross-modality task on the MMWHS dataset without utilizing any target-domain labels for model selection, confirming its robustness and generalizability for diverse clinical environments.
Abstract (translated)
尽管深度学习在计算机辅助肺栓塞（PE）诊断中展示了很大的潜力，但在计算机断层扫描肺血管造影（CTPA）中的实际部署通常会受到“领域偏移”和专家标注成本过高的限制。为了解决这些挑战，提出了一种无监督域自适应（UDA）框架，该框架采用了Transformer骨干网络以及Mean-Teacher架构来进行跨中心的语义分割。主要关注通过学习特征空间内的深层结构信息来提高伪标签的可靠性。为此，设计并集成了三个模块：(1) 原型对齐(PA)机制以减少类别级别的分布差异；(2) 全局和局部对比学习(GLCL)，用于捕捉像素级拓扑关系以及全局语义表示；(3) 一种基于注意力的辅助局部预测(AALP)模块，通过自动从Transformer注意图中提取高信息切片来增强对小PE病灶的敏感性。 在跨中心数据集（FUMPE和CAD-PE）上的实验验证显示了显著性能提升。在FUMPE到CAD-PE的任务中，IoU从0.1152增加到了0.4153；而在CAD-PE到FUMPE的任务中，则是从0.1705提高至了0.4302。此外，在MMWHS数据集上的CT到MRI跨模态任务中，所提出的方法在没有使用任何目标领域标签的情况下进行模型选择时，达到了69.9%的Dice分数，这证实了其在多样化的临床环境中的鲁棒性和泛化能力。
URL
https://arxiv.org/abs/2602.19891
