{
  "abstract": "Breast cancer remains a major cause of cancer-related morbidity and mortality worldwide, and histopathological examination remains the diagnostic gold standard. This study presents HistoBreastCAD, an integrated deep learning framework that combines tissue-region segmentation, four-class histopathology classification, spatial mapping of predictions, and interactive whole-slide image (WSI) review. Segmentation was evaluated using WSI-grouped five-fold cross-validation, while classification used source-image-grouped five-fold cross-validation to prevent source-level data leakage. The final DeepLabV3+ L0 segmentation configuration achieved an out-of-fold micro-Dice of 0.8390, IoU of 0.7226, precision of 0.8278, and recall of 0.8505. For classification, EfficientNetB0 was the strongest individual model, while a frozen equal-weight soft-voting ensemble of EfficientNetB0 and ResNet18-GAP-attention achieved an accuracy of 0.8283 ± 0.0320 and macro-F1 of 0.8282 ± 0.0316, outperforming EfficientNetB0 in all five validation folds. An exploratory reader study using the 100 microscopy images from the held-out BACH test set showed moderate interobserver agreement between two pathologists (Cohen’s κ = 0.5105; n = 95), whereas model-to-reader concordance was lower. The ensemble did not increase reader concordance relative to EfficientNetB0. HistoBreastCAD therefore demonstrates the feasibility of integrating established deep learning components into a source-separated WSI-oriented digital pathology workflow. However, external multi-institutional validation, adjudicated reader studies, and formal explainability assessment are required before claims of clinical generalizability or utility can be supported.",
  "abstract_shown": true,
  "affiliations": [
    "Departamento de Ciencias de La Computación, Universidad Técnica Particular de Loja, Loja, Ecuador",
    "Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, Loja, Ecuador",
    "Corporación Médica Monte Sinaí-CIPAM (Centro Integral de Patología Mamaria) Cuenca-Ecuador, Facultad de Ciencias Médicas, Universidad de Cuenca, Cuenca, Ecuador",
    "Departamento de Ciencias de La Salud, Universidad Técnica Particular de Loja, Loja, Ecuador"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Oswaldo Celi-Vega"
    },
    {
      "affs": [
        1
      ],
      "name": "Yuliana Jiménez-Gaona"
    },
    {
      "affs": [
        2
      ],
      "name": "Patricio Corral-Dominguez"
    },
    {
      "affs": [
        2
      ],
      "name": "Ángel Gutiérrez"
    },
    {
      "affs": [
        3
      ],
      "name": "Zaida Socola-Barzallo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/275qn1mj14jyy.bib",
  "cited_by": 0,
  "doi": "10.3389/fbinf.2026.1870283",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13710403/",
  "id": "275qn1mj14jyy",
  "issns": [
    "2673-7647"
  ],
  "landing_url": "https://doi.org/10.3389/fbinf.2026.1870283",
  "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",
      "Original Research"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7221655712",
  "pdf_url": "https://www.frontiersin.org/journals/bioinformatics/articles/10.3389/fbinf.2026.1870283/pdf",
  "pmcid": "PMC13710403",
  "pmid": "42947913",
  "published": "2026-10-13",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/275qn1mj14jyy.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:23Z",
      "native_id": "PMC13710403",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fbinf.2026.1870283"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "journal-openalex:1312|Genomics and Phylogenetic Studies",
      "subfield": "molecular-biology"
    }
  ],
  "title": "HistoBreastCAD: an integrated deep learning framework for segmentation and multiclass classification of whole-slide breast histopathology images",
  "title_text": "HistoBreastCAD: an integrated deep learning framework for segmentation and multiclass classification of whole-slide breast histopathology images",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:23Z",
  "url": "https://research.draconitic.com/paper/275qn1mj14jyy",
  "venue": "Frontiers in Bioinformatics",
  "versions": []
}