Reliability of foundation models for image retrieval in histopathology.

Abubakr Shafique, Xiaoli Qin, Amanda Dy, Najd Alshamlan, Dimitrios Androutsos, Susan J Done, April Khademi

Abstract read

In one paragraph

Article in Npj imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Abubakr ShafiqueDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada. abubakr.shafique@torontomu.ca.

Xiaoli QinDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.

Amanda DyDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.

Najd AlshamlanKing Abdullah bin Abdulaziz University Hospital, Princess Norah University, Riyadh, Saudi Arabia.

Dimitrios AndroutsosDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.

Susan J DoneLaboratory Medicine Program, University Health Network, Toronto, ON, Canada.

April KhademiDepartment of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada. akhademi@torontomu.ca.

Funding

Canadian Cancer Society 707155Mitacs IT33068

6 · The paper itself

Abstract

Ensuring fairness and explainability is essential for the development of ethical, reliable, and effective AI systems in healthcare. Bias in AI models can contribute to disparities in clinical outcomes, challenging equity in medical decision-making. Content-Based Image Retrieval (CBIR) offers interpretable, visual tools to support diagnostic processes; however, these tools remain susceptible to biases inherent in the data. This study investigates covariate bias arising from differences in scanning devices within Foundation Models (FMs) used for CBIR in histopathology. We introduce a unique dataset comprising spatially co-registered images derived from the same histopathology slides scanned using two distinct scanners. This design enables a targeted analysis of scanner-induced variability in FM representations. Among the FMs assessed, Vision Transformer (ViT)-based architectures such as UNI, Virchow2, and GigaPath, demonstrated top performance and best generalization properties across scanners.

Identifiers

PMID42191871

DOI10.1038/s44303-026-00174-7

PMCPMC13241532

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.