{
  "abstract": "Motivation: Automated cytology screening is essential for early cervical cancer prevention, where gigapixel whole-slide images (WSIs) are typically partitioned into tiles for tile-level feature extraction and subsequent WSI-level diagnosis aggregation. However, existing tile-level methods remain constrained by limited input sizes, which frequently obscures sparse and morphologically subtle abnormal cells. Furthermore, these methods commonly formulate tile-level representation learning as a coarse single-label classification task, thereby failing to capture the complex co-occurrence patterns and morphological heterogeneity of cervical cytological abnormalities.\n\nResults: We propose the Frequency-Aware Multi-Label Learning Network (FMCerNet), a tile-level feature extractor equipped with a Large-Field Frequency-Enhanced ViT backbone and a Coarse-to-Fine multi-label classification head. The former incorporates a Dual-Tree Complex Wavelet Transform module to efficiently extract frequency-domain features from large-scale tiles, whereas the latter enhances fine-grained tile representation decoding through hierarchical supervision. Experiments on both public and private datasets demonstrate that FMCerNet improves the accuracy of positive screening and fine-grained subtype classification while maintaining high inference speed.\n\nAvailability and implementation: Code at https://github.com/imAzhou/FMCerNet and archived on Zenodo (doi: 10.5281/zenodo.22886105).",
  "abstract_shown": true,
  "affiliations": [
    "College of Computer Science, Zhejiang University , Hangzhou, Zhejiang 310058,",
    "Department of Pathology, The First Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, Zhejiang 310003,",
    "School of Software Technology, Zhejiang University , Ningbo, Zhejiang 315100,",
    "International Peace Maternal and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University , Shanghai 200030,"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Linyun Zhou",
      "orcid": "0009-0004-5057-3701"
    },
    {
      "affs": [
        1
      ],
      "name": "Qiuyu Cai"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiaoling Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Weihan Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Jian Yang"
    },
    {
      "affs": [
        0
      ],
      "name": "Mingli Song"
    },
    {
      "affs": [
        1
      ],
      "name": "Jing Zhang"
    },
    {
      "affs": [
        3
      ],
      "name": "Feng Zhou"
    },
    {
      "affs": [
        1
      ],
      "name": "Qihan You"
    },
    {
      "affs": [
        3
      ],
      "name": "Yudong Wang"
    },
    {
      "affs": [
        2
      ],
      "name": "Zunlei Feng"
    },
    {
      "affs": [
        1
      ],
      "name": "Xiuming Zhang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/191mtancasq8h.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag759",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13716490/",
  "id": "191mtancasq8h",
  "issns": [
    "1367-4803",
    "1367-4811"
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  "landing_url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag759/8943329",
  "language": "en",
  "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": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Bioimage Informatics"
    ],
    [
      "heading",
      "Original Paper"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7222943549",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag759/72109238/btag759.pdf",
  "pmcid": "PMC13716490",
  "pmid": "42895261",
  "published": "2026-10-19",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/191mtancasq8h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag759",
      "source": "crossref",
      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag759/8943329"
    },
    {
      "first_seen": "2026-11-01T12:23:25Z",
      "native_id": "PMC13716490",
      "source": "pmc",
      "url": "https://doi.org/10.1093/bioinformatics/btag759"
    }
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  "subjects": [
    {
      "field": "biology",
      "origin": "issn:1367-4803",
      "subfield": "bioinformatics"
    }
  ],
  "title": "FMCerNet: frequency-aware multi-label learning for fine-grained cervical cytology screening",
  "title_text": "FMCerNet: frequency-aware multi-label learning for fine-grained cervical cytology screening",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:23:25Z",
  "url": "https://research.draconitic.com/paper/191mtancasq8h",
  "venue": "Bioinformatics",
  "versions": []
}