PuSH - Publication Server of Helmholtz Zentrum München: HASD: Hierarchical Adaption for Pathology Slide-Level Domain-Shift.

Information

Clues to quality of journals

Open Access Policy of Helmholtz Association 2016

CC Licencences

Publication Metrics

Navigation

Home

Deutsch

EVA: Electronic Publishing

New EVA Application

Research

Advanced Search

Browse by ...

... HMGU-Authors/Consortia

... Organizational Structure

... Journal

... Publication Type

... Research Data

... Arbeitsgruppen

... Publication Year

Publication overview

Statistics

Statistics (last 5 years)

OA Publications

Publish

Submit Publication

Import publication from...

...EVA

Report missing publication

Highlights

Support & Contact

Contact persons

Help

Data protection

Helmholtz Open Science

JANE Journal Estimator

SHERPA/RoMEO

DOAJ

Export:

Text

Endnote (RIS) BIB

BibTeX

Liu, J.* ; Li, H.* ; Yang, C. ; Deutges, M. ; Sadafi, A. ; You, X.* ; Breininger, K.* ; Navab, N.* ; Schüffler, P.J.*

HASD: Hierarchical Adaption for Pathology Slide-Level Domain-Shift.

In: (28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025, 23-27 September 2025, Daejeon). Berlin [u.a.]: Springer, 2026. 332-342 (Lect. Notes Comput. Sc. ; 15965 LNCS)

DOI

Abstract
Metrics
Extra information

Domain shift is a critical problem for artificial intelligence (AI) in pathology as it is heavily influenced by center-specific conditions. Current pathology domain adaptation methods focus on image patches rather than whole-slide images (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. Code is available at https://github.com/TumVink/HASD.

Altmetric