{
  "abstract": "Accurate breast lesion segmentation in ultrasound is difficult because speckle noise, heterogeneous lesion appearance, and poorly defined margins can obscure clinically relevant boundaries. This work presents UGB-SegNet, an uncertainty-guided breast ultrasound segmentation network that combines four components: multi-scale convolutional block attention (CBAM) at three EfficientNet-B0 encoder stages, a learnable feature pyramid network for adaptive multi-scale fusion, an entropy-guided decoder that routes fine-grained encoder information toward uncertain regions, and a boundary-aware composite loss with deep supervision. The model was evaluated on the public BUSI dataset (647 annotated lesion images) using a stratified 454/64/129 train/validation/test split. On the held-out test set, UGB-SegNet achieved 81.30% Dice, 68.49% IoU, 81.71% sensitivity, 98.14% specificity, and 15.63-pixel HD95. Under identical training conditions it outperformed five directly trained baselines: U-Net, Attention U-Net, SegNet, DeepLabV3+, and TransUNet. A five-fold resampling experiment over the development set with the held-out test set fixed yielded a mean Dice of 76.36% +/-1.66% (95% CI 74.90-77.81). Reported ablations attribute gains of +2.06 Dice points to multi-scale CBAM, +1.52 to learnable FPN fusion, +1.22 to the uncertainty-guided decoder, and +1.24 to boundary-aware training. UGB-SegNet contains 14.00M parameters and runs at 62.4 FPS at 224 x 224 resolution on the tested NVIDIA GPU, providing a compact accuracy-efficiency trade-off for future prospective clinical validation.",
  "abstract_shown": true,
  "affiliations": [
    "Feng chia university , Taichung ( Taiwan)"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "T. Alam"
    },
    {
      "name": "F. R. Hsu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cx3ffprafhbfz.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.03.26364633",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.03.26364633v1.full",
  "id": "cx3ffprafhbfz",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.03.26364633v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "radiology and imaging"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7220465135",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.03.26364633v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cx3ffprafhbfz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:06Z",
      "native_id": "10.64898/2026.10.03.26364633",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.03.26364633v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:radiology and imaging",
      "subfield": "imaging"
    }
  ],
  "title": "UGB-SegNet: Uncertainty-Guided Multi-Scale Attention and Boundary-Aware Learning for Breast Ultrasound Lesion Segmentation",
  "title_text": "UGB-SegNet: Uncertainty-Guided Multi-Scale Attention and Boundary-Aware Learning for Breast Ultrasound Lesion Segmentation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:06Z",
  "url": "https://research.draconitic.com/paper/cx3ffprafhbfz",
  "venue": "medRxiv",
  "versions": []
}