{
  "abstract": "Automated triage of whole-slide cervical cytology images remains constrained by the heavy dependence on single-cell bounding boxes or pixel-level annotations, which introduce significant bottlenecks, high expert overhead, and observer variability. To address these challenges, we present a weakly-supervised, detection-free Multiple Instance Learning (MIL) framework powered by a Gated Attention pooling engine that operates exclusively on slide-level predictions. By treating whole-slide images as bags of local instance patches, the dual-branch gated network dynamically assigns non-linear attention weights to highlight isolated dysplastic cells while suppressing benign background and debris, successfully resolving the \"needle-in-a-haystack\" problem inherent to high-grade lesions. Evaluated across multi-center cohorts including internal datasets (SIPaKMeD, Herlev, and CRIC) and the unannotated, out-of-distribution Mendeley LBC validation cohort processed via an unsupervised marker-controlled watershed pipeline our approach demonstrates robust generalization without requiring dense instance labels. Furthermore, our architectural comparison reveals a key trade-off: lightweight backbones like MobileNetV2 optimize in-distribution multi-center accuracy (90.96% Accuracy, 0.9800 ROC-AUC), whereas higher-capacity models like Xception ensure superior out-of-distribution robustness (80.43% Balanced Accuracy) under domain shifts. Ultimately, this detection-less pipeline provides a scalable and clinically viable solution to automate slide triage and alleviate cytopathologist shortages in resource-constrained environments.\n\nAuthor summaryAutomated screening of cervical cytology slides is traditionally hindered by the need for labor-intensive, pixel-level cell annotations. To overcome this limitation, this study introduces a weakly-supervised, detection-free Multiple Instance Learning framework that performs slide triage using only slide-level diagnostic labels. By employing a Gated Attention mechanism, the model dynamically focuses on informative cellular regions while ignoring background debris, successfully addressing the challenge of identifying rare dysplastic cells across gigapixel images. Evaluations across multi-center datasets demonstrate that lightweight models like MobileNetV2 achieve high accuracy on in-distribution data, whereas deeper architectures like Xception provide superior robustness against out-of-distribution variations. Ultimately, this approach offers a scalable, annotation-free triage tool designed to assist cytopathologists and improve screening workflows in resource-constrained environments.",
  "abstract_shown": true,
  "affiliations": [
    "Muhimbili University of Health and Allied Sciences"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Mayala"
    },
    {
      "name": "S. Sang'udi"
    },
    {
      "name": "D. Mzurikwao"
    },
    {
      "name": "E. Suluba"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3e4mab48mv4ws.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.29.26364279",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364279v1.full",
  "id": "3e4mab48mv4ws",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364279v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7214989523",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364279v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3e4mab48mv4ws.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:48Z",
      "native_id": "10.64898/2026.09.29.26364279",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.09.29.26364279v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Weakly-Supervised Gated Attention Multiple Instance Learning for Multi-Center Cervical Cytology Slide Triage",
  "title_text": "Weakly-Supervised Gated Attention Multiple Instance Learning for Multi-Center Cervical Cytology Slide Triage",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:48Z",
  "url": "https://research.draconitic.com/paper/3e4mab48mv4ws",
  "venue": "medRxiv",
  "versions": []
}