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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
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.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Domain Shift ; Pathology Slide-level Tasks
Sprache englisch
Veröffentlichungsjahr 2026
HGF-Berichtsjahr 2026
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel 28th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2025
Konferzenzdatum 23-27 September 2025
Konferenzort Daejeon
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15965 LNCS, Heft: , Seiten: 332-342 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Institut(e) Institute of AI for Health (AIH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540007-001
DOI 10.1007/978-3-032-04978-0_32
Scopus ID 105017845919
Erfassungsdatum 2025-10-23
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