{
  "abstract": "PURPOSE: To develop and cross-dataset validate a hybrid ResNet50-Transformer U-Net for automated segmentation of breast cancer (BC) lesions on 2D dynamic contrast-enhanced MRI (DCE-MRI). METHODS: A total of 20434 BC MRI images from the BC MRI Segmentation Benchmark (BC-MRI-SEG), comprising 4 independent public datasets, were included. A strict patient-level data splitting strategy was applied to avoid data leakage. The proposed model was based on a U-Net architecture integrating a ResNet50 encoder for local feature extraction and a bottleneck Transformer module for global contextual modeling. Model performance was evaluated using a leave-one-dataset-out cross-dataset external validation strategy. Segmentation performance was assessed using the Dice similarity coefficient (DSC), intersection over union (IoU), 95% Hausdorff distance (HD95), sensitivity, and precision. RESULTS: The proposed hybrid model achieved stable segmentation performance across the evaluated datasets. In leave-one-dataset-out external validation, patient-level DSC ranged from 0.75 ± 0.13 to 0.79 ± 0.10, while IoU ranged from 0.61 ± 0.15 to 0.66 ± 0.12. In the fixed held-out comparison cohort, the proposed model achieved a DSC of 0.82 ± 0.08 and an IoU of 0.70 ± 0.10 and outperformed the evaluated baseline models (Holm-adjusted P ≤ 0.021). Qualitative analysis showed generally close agreement between the predicted masks and reference annotations across lesions with different morphologic appearances. CONCLUSION: The proposed ResNet-Transformer-based U-Net framework provided a robust approach for automated BC MRI segmentation across heterogeneous public datasets. These findings suggest that combining local feature extraction with global contextual modeling may be useful for computer-assisted breast MRI analysis and related clinical workflows.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "Yongjian Sui"
    },
    {
      "name": "Peiru Li"
    },
    {
      "name": "Jiacheng Li"
    },
    {
      "name": "Peng Zhou"
    },
    {
      "name": "Xiaolong Zheng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8247ga3z6za9q.bib",
  "cited_by": 0,
  "doi": "10.2463/mrms.mp.2026-0099",
  "fulltext_url": "https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_article",
  "id": "8247ga3z6za9q",
  "issns": [
    "1880-2206",
    "1347-3182"
  ],
  "landing_url": "https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_article",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "jstage-journal",
      "medicine/imaging"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7220934634",
  "pdf_url": "https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8247ga3z6za9q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:42Z",
      "native_id": "10.2463/mrms.mp.2026-0099",
      "source": "jstage",
      "url": "https://www.jstage.jst.go.jp/article/mrms/25/4/25_mp.2026-0099/_article"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "jstage-journal:medicine/imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Deep Learning–driven Segmentation of Breast Cancer MR Imaging Using a Hybrid ResNet–Transformer Architecture and Cross-dataset Validation",
  "title_text": "Deep Learning–driven Segmentation of Breast Cancer MR Imaging Using a Hybrid ResNet–Transformer Architecture and Cross-dataset Validation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:42Z",
  "url": "https://research.draconitic.com/paper/8247ga3z6za9q",
  "venue": "Magnetic Resonance in Medical Sciences",
  "versions": []
}