{
  "abstract": "Accurate skin lesion segmentation remains difficult when dermoscopic boundaries are fragmented by hair occlusion, uneven illumination, or weak local contrast. To address this problem, the authors propose Swin-WaveNet, a frequency-guided encoder-decoder that combines a Swin Transformer backbone with wavelet decomposition and selective state-space modeling. Rather than passing skip features directly to the decoder, the proposed method separates low-frequency semantic information from high-frequency details, selectively refines boundary-related responses, and suppresses artifact-induced activation prior to feature reconstruction. The main results are reported as the mean and standard deviation of the sample over three runs. Swin-WaveNet achieved a Dice score of 89.69% ± 0.51% and a 95th-percentile Hausdorff distance (HD95) of [Formula: see text] pixels on the International Skin Imaging Collaboration (ISIC) 2018 dataset. Without fine-tuning on ISIC 2017, it obtained a Dice score of 91.69% ± 1.36% and an HD95 of [Formula: see text] pixels. On Kvasir-SEG, the model achieved a Dice score of 91.63% ± 0.57% and an HD95 of [Formula: see text] pixels. In a single-run component ablation, the complete model improved the Dice score by 0.96 percentage points and reduced HD95 by 1.16 pixels relative to the internal baseline. These results indicate that frequency-selective feature processing can help preserve weak or interrupted lesion boundaries.",
  "abstract_shown": true,
  "affiliations": [
    "Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, Hubei, 443002, China.",
    "College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, 443002, China.",
    "Yichang Key Laboratory of Intelligent Medicine, China Three Gorges University, Yichang, Hubei, 443002, China.",
    "Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang, Hubei, 443002, China. tangtinglong@ctgu.edu.cn.",
    "College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei, 443002, China. tangtinglong@ctgu.edu.cn.",
    "Yichang Key Laboratory of Intelligent Medicine, China Three Gorges University, Yichang, Hubei, 443002, China. tangtinglong@ctgu.edu.cn.",
    "Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, Guangdong, 519087, China.",
    "School of Information Science and Technology, Hangzhou Normal University, Hangzhou, Zhejiang, 311121, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
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      "name": "Zhiping Dan"
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      "name": "Run Zhang"
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      "name": "Tinglong Tang",
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      "name": "Yirong Wu"
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      "name": "Xiaosheng Yu"
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      "name": "Shuifa Sun"
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    {
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      ],
      "name": "Ben Wang"
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  "bibtex": "https://research.draconitic.com/paper/19jv98hnfwhbr.bib",
  "cited_by": 0,
  "doi": "10.1186/s42492-026-00235-z",
  "fulltext_url": "https://europepmc.org/articles/PMC13680583",
  "id": "19jv98hnfwhbr",
  "issns": [
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    "2524-4442"
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  "landing_url": "https://europepmc.org/article/MED/42895593",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
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  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "pdf_url": "https://europepmc.org/articles/PMC13680583?pdf=render",
  "pmcid": "PMC13680583",
  "pmid": "42895593",
  "published": "2026-10-19",
  "publisher": "SpringerOpen",
  "ris": "https://research.draconitic.com/paper/19jv98hnfwhbr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:32Z",
      "native_id": "MED:42895593",
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      "url": "https://europepmc.org/article/MED/42895593"
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      "first_seen": "2026-10-30T13:45:32Z",
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  "subjects": [
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      "field": "medicine",
      "origin": "openalex:2730|Cutaneous Melanoma Detection and Management",
      "subfield": "oncology"
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  ],
  "title": "Swin-WaveNet: frequency-guided boundary restoration for robust skin lesion segmentation.",
  "title_text": "Swin-WaveNet: frequency-guided boundary restoration for robust skin lesion segmentation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:32Z",
  "url": "https://research.draconitic.com/paper/19jv98hnfwhbr",
  "venue": "Visual computing for industry, biomedicine, and art",
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}