HASD: Hierarchical Adaption for pathology Slide-level Domain-shift
Journal:
arXiv
Published Date:
Jun 30, 2025
Abstract
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.
