Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models

• URL: http://arxiv.org/abs/2601.04163v1
• Date: Wed, 07 Jan 2026 18:24:12 GMT
• Title: Scanner-Induced Domain Shifts Undermine the Robustness of Pathology Foundation Models
• Authors: Erik Thiringer, Fredrik K. Gustafsson, Kajsa Ledesma Eriksson, Mattias Rantalainen,
• Abstract summary: Pathology foundation models (PFMs) have become central to computational pathology.<n>Despite strong benchmark performance, PFM robustness to real-world technical domain shifts remains poorly understood.<n>We evaluate the robustness of 14 PFMs to scanner-induced variability.
• Score: 3.8310079617300876
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pathology foundation models (PFMs) have become central to computational pathology, aiming to offer general encoders for feature extraction from whole-slide images (WSIs). Despite strong benchmark performance, PFM robustness to real-world technical domain shifts, such as variability from whole-slide scanner devices, remains poorly understood. We systematically evaluated the robustness of 14 PFMs to scanner-induced variability, including state-of-the-art models, earlier self-supervised models, and a baseline trained on natural images. Using a multiscanner dataset of 384 breast cancer WSIs scanned on five devices, we isolated scanner effects independently from biological and laboratory confounders. Robustness is assessed via complementary unsupervised embedding analyses and a set of clinicopathological supervised prediction tasks. Our results demonstrate that current PFMs are not invariant to scanner-induced domain shifts. Most models encode pronounced scanner-specific variability in their embedding spaces. While AUC often remains stable, this masks a critical failure mode: scanner variability systematically alters the embedding space and impacts calibration of downstream model predictions, resulting in scanner-dependent bias that can impact reliability in clinical use cases. We further show that robustness is not a simple function of training data scale, model size, or model recency. None of the models provided reliable robustness against scanner-induced variability. While the models trained on the most diverse data, here represented by vision-language models, appear to have an advantage with respect to robustness, they underperformed on downstream supervised tasks. We conclude that development and evaluation of PFMs requires moving beyond accuracy-centric benchmarks toward explicit evaluation and optimisation of embedding stability and calibration under realistic acquisition variability.

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