HASD: Hierarchical Adaption for pathology Slide-level Domain-shift

TL;DR

This paper introduces HASD, a hierarchical framework for slide-level domain adaptation in pathology AI, addressing global WSI features and domain shifts across datasets with improved accuracy and efficiency.

Contribution

The paper proposes a novel hierarchical adaptation framework with multi-scale feature alignment and a prototype selection mechanism for efficient slide-level domain adaptation.

Findings

Achieved 4.1% AUROC improvement in breast cancer grading

Gained 3.9% C-index in UCEC survival prediction

Validated on five datasets across two tasks

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.