UWarp: A Whole Slide Image Registration Pipeline to Characterize Scanner-Induced Local Domain Shift
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
Histopathology slide digitization introduces scanner-induced domain shift
that can significantly impact computational pathology models based on deep
learning methods. In the state-of-the-art, this shift is often characterized at
a broad scale (slide-level or dataset-level) but not patch-level, which limits
our comprehension of the impact of localized tissue characteristics on the
accuracy of the deep learning models. To address this challenge, we present a
domain shift analysis framework based on UWarp, a novel registration tool
designed to accurately align histological slides scanned under varying
conditions. UWarp employs a hierarchical registration approach, combining
global affine transformations with fine-grained local corrections to achieve
robust tissue patch alignment. We evaluate UWarp using two private datasets,
CypathLung and BosomShieldBreast, containing whole slide images scanned by
multiple devices. Our experiments demonstrate that UWarp outperforms existing
open-source registration methods, achieving a median target registration error
(TRE) of less than 4 pixels (<1 micrometer at 40x magnification) while
significantly reducing computational time. Additionally, we apply UWarp to
characterize scanner-induced local domain shift in the predictions of
Breast-NEOprAIdict, a deep learning model for breast cancer pathological
response prediction. We find that prediction variability is strongly correlated
with tissue density on a given patch. Our findings highlight the importance of
localized domain shift analysis and suggest that UWarp can serve as a valuable
tool for improving model robustness and domain adaptation strategies in
computational pathology.
