解决病理切片级域偏移问题，提升跨中心诊断可靠性
HASD: Hierarchical Adaption for pathology Slide-level Domain-shift
- 分层自适应框架融合全局与局部特征对齐
- 乳腺癌分级任务AUROC提升4.1%，生存预测C-index提升3.9%
- 计算高效，适合临床机构部署
病理AI面临严重的域偏移问题，因数据受中心特异性条件影响。现有方法多关注图像块而非全切片（WSI），难以捕捉临床所需的全局特征。本文提出针对切片级域偏移的分层自适应框架HASD，通过两级机制实现多尺度特征一致性：一是分层适配框架，包含域对齐求解器、切片级几何不变性正则化和块级注意力一致性正则化；二是原型选择机制降低计算开销。在五个数据集上的两个切片级任务中验证，乳腺癌HER2分级任务中AUROC提升4.1%，子宫内膜癌生存预测任务中C-index提升3.9%。该方法为病理机构提供低成本、高可靠的切片级域适应方案。
原文摘要 · Abstract (English)
Domain shift is a critical problem for pathology AI as pathology data is heavily influenced by center-specific conditions. Current pathology domain adaptation methods focus on image patches rather than WSI, thus failing to capture global WSI features required in typical clinical scenarios. In this work, we address the challenges of slide-level domain shift by proposing a Hierarchical Adaptation framework for Slide-level Domain-shift (HASD). HASD achieves multi-scale feature consistency and computationally efficient slide-level domain adaptation through two key components: (1) a hierarchical adaptation framework that integrates a Domain-level Alignment Solver for feature alignment, a Slide-level Geometric Invariance Regularization to preserve the morphological structure, and a Patch-level Attention Consistency Regularization to maintain local critical diagnostic cues; and (2) a prototype selection mechanism that reduces computational overhead. We validate our method on two slide-level tasks across five datasets, achieving a 4.1\% AUROC improvement in a Breast Cancer HER2 Grading cohort and a 3.9\% C-index gain in a UCEC survival prediction cohort. Our method provides a practical and reliable slide-level domain adaption solution for pathology institutions, minimizing both computational and annotation costs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。