{
  "abstract": "Background: Artifacts in digital pathology whole-slide images (WSIs) cause diagnostic delay and quality issues, yet quality control (QC) in most pathology laboratories remains manual, subjective, and difficult to scale. We present BigpictureQC, a deep learning framework for WSI quality assessment based on per-class pixel-level segmentation of six visually distinct artifact and marking classes.\n\nMethods: BigpictureQC was trained on 268 WSIs from five data sources and evaluated on >9,200 WSIs spanning human and preclinical samples, diverse tissue types, hematoxylin and eosin (H&E) and immunohistochemical (IHC) stains, and multiple scanner models. Patch-level segmentation was evaluated on 569 WSIs from seven independent cohorts using the Dice similarity coefficient for pixel-level segmentation; slide-level artifact detection was assessed on an independent dataset of 315 dermatopathology WSIs using F1 scores. A quantitative slide-level quality score was derived from artifact coverage and evaluated against manual quality ratings using Spearman rank correlation and AUC. Clinical impact was evaluated in a retrospective cohort of 3,686 diagnostic slides with technician rescan decisions and a prospective cohort of 4,426 unreviewed slides from routine diagnostics.\n\nResults: BigpictureQC achieved a mean Dice score of 0.93 on the evaluation set, outperforming academic and commercial baselines across all artifact classes and domains (H&E, IHC, clinical, preclinical). Slide-level artifact detection yielded a micro-averaged F1 score of 0.93. The derived quality score strongly correlated with manual ratings and achieved an AUC of 0.978 for identifying poor-quality slides. In clinical workflow analyses, automated quality control identified 93% of poor-quality slides, compared with 35% detected by routine manual inspection. In the prospective-style cohort, 8.2% of slides were identified as having substantial quality issues that would otherwise have reached pathologists without prior review.\n\nConclusions: BigpictureQC provides the first publicly available automated QC framework validated across human and preclinical digital pathology, encompassing multiple tissue types, H&E and IHC stains, and diverse scanner models. It substantially outperforms existing manual and automated approaches in detecting poor-quality slides, and demonstrates strong potential to improve laboratory efficiency by enabling systematic pre-screening of all digitized slides at the point of scanning. The code, inference pipeline and trained model weights are publicly released.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "name": "Marina D’Amato"
    },
    {
      "name": "Holger Hoefling"
    },
    {
      "name": "Erio Barale-Thomas"
    },
    {
      "name": "Freya Molenberghs"
    },
    {
      "name": "Pieter Van Nuffel"
    },
    {
      "name": "Anna Bodén"
    },
    {
      "name": "Paul van Diest"
    },
    {
      "name": "Nikolas Stathonikos"
    },
    {
      "name": "Sara Rombouts"
    },
    {
      "name": "Ivan R. Slootweg"
    },
    {
      "name": "Geert Litjens"
    },
    {
      "name": "Jeroen van der Laak"
    },
    {
      "name": "Francesco Ciompi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1qe6er8eae66q.bib",
  "cited_by": 0,
  "doi": "10.1186/s12916-026-05257-1",
  "fulltext_url": null,
  "id": "1qe6er8eae66q",
  "issns": [
    "1741-7015"
  ],
  "landing_url": "https://link.springer.com/article/10.1186/s12916-026-05257-1",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/1qe6er8eae66q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:53Z",
      "native_id": "10.1186/s12916-026-05257-1",
      "source": "crossref",
      "url": "https://link.springer.com/article/10.1186/s12916-026-05257-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "journal-openalex:F27",
      "subfield": "general"
    }
  ],
  "title": "BigpictureQC: multi-stain, multi-species artifact segmentation for whole-slide quality control",
  "title_text": "BigpictureQC: multi-stain, multi-species artifact segmentation for whole-slide quality control",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:21:02Z",
  "url": "https://research.draconitic.com/paper/1qe6er8eae66q",
  "venue": "BMC Medicine",
  "versions": []
}