Evidence map›Paper›PMID 42328493›Full record

ArticleIEEE open journal of engineering in medicine and biology2026

Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation.

Michael Yeung, Todd Watts, Sean Yw Tan, Peiyuan Jing, Pedro F Ferreira, Andrew D Scott, Sonia Nielles-Vallespin, Guang Yang

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In one paragraph

Article in IEEE open journal of engineering in medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

8 authors.

Michael YeungDepartment of ComputingImperial College London SW7 2AZ London U.K.
Todd WattsSignalling ProgrammeBabraham Institute CB22 3AT Cambridge U.K.ORCID https://orcid.org/0000-0001-8345-4187
Sean Yw TanDepartment of Clinical NeurosciencesUniversity of Cambridge CB2 2PY Cambridge U.K.ORCID https://orcid.org/0000-0002-3150-6069
Peiyuan JingDepartment of Bioengineering and Imperial-XImperial College London W12 7SL London U.K.ORCID https://orcid.org/0009-0009-1685-5285
Pedro F FerreiraNational Heart & Lung InstituteImperial College London SW3 6LY London U.K.ORCID https://orcid.org/0000-0002-0436-3496
Andrew D ScottNational Heart & Lung InstituteImperial College London SW3 6LY London U.K.
Sonia Nielles-VallespinNational Heart & Lung InstituteImperial College London SW3 6LY London U.K.
Guang YangDepartment of Bioengineering and Imperial-XImperial College London W12 7SL London U.K.ORCID https://orcid.org/0000-0001-7344-7733

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stain variation poses a major challenge for automated digital pathology. Numerous techniques address this issue, yet show limited success, especially outside H&E stains and classification tasks. We propose Stain Consistency Learning (SCL), combining stain-specific augmentation and a novel consistency loss to learn stain-invariant features. We conduct the first large-scale evaluation of ten methods on Massons trichrome and H&E datasets for segmentation. Our results demonstrate that traditional stain normalization offers little benefit, while stain augmentation and adversarial learning significantly improve performance. SCL consistently outperforms all other methods.

Indexed as

Computational pathologydomain adaptationinstance segmentationstain augmentationstain normalization

Identifiers

PMID42328493
PMCPMC13278751

What OpenQuestion holds

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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.