{
  "abstract": "Privacy-preserving collaborative medical image analysis across multiple healthcare institutions remains challenging due to data heterogeneity, patient privacy constraints, and incomplete cross-institutional record alignment. To address these limitations, this paper proposes MST-Former, a multiparty shared transformer framework that enables secure embedding fusion without sharing raw medical images. The framework combines a deep reinforced autoencoder (DRA) for local representation learning with adaptive positional encoding, dynamic attention masking, and transformer-based embedding aggregation to facilitate robust cross-institutional knowledge integration. In addition, a contrastive learning strategy is employed to enhance representation consistency across heterogeneous imaging sources. Extensive experiments conducted on multiple benchmark medical imaging datasets demonstrate that MST-Former consistently outperforms existing methods in segmentation and prediction performance while preserving data confidentiality. Across multiple medical image segmentation datasets, MST-Former consistently achieved strong performance, with mDice scores ranging from 92.73 to 94.95% and mIoU scores ranging from 87.31 to 92.65%, demonstrating its effectiveness and generalization capability. These results highlight the effectiveness of MST-Former as a scalable and privacy-preserving solution for collaborative medical image analysis in distributed healthcare environments.",
  "abstract_shown": true,
  "affiliations": [
    "Information Technology Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)",
    "Saudi Electronic University",
    "College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU)",
    "Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University",
    "Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University",
    "Department of Information System, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University",
    "Department of Intelligent Systems, KTH Royal Institute of Technology"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Shakir Khan"
    },
    {
      "affs": [
        1
      ],
      "name": "Thamer Alshammari"
    },
    {
      "affs": [
        2
      ],
      "name": "Fahad Omar Alomary"
    },
    {
      "affs": [
        3
      ],
      "name": "Hadeel Alsolai"
    },
    {
      "affs": [
        4
      ],
      "name": "Sultan Ahmad"
    },
    {
      "affs": [
        5
      ],
      "name": "Bayan Ibrahimm Alabduallah"
    },
    {
      "affs": [
        3
      ],
      "name": "Fatimah Alhayan"
    },
    {
      "affs": [
        6
      ],
      "name": "Saqib Qamar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dq5yh8bzhvcgn.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-72504-z",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-72504-z",
  "id": "dq5yh8bzhvcgn",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-72504-z",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
    ]
  ],
  "openalex": "W7214798295",
  "pdf_url": "https://www.nature.com/articles/s41598-026-72504-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/dq5yh8bzhvcgn.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:47Z",
      "native_id": "9f65fda8398f41529404e165942e2854",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-72504-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "MST-former: multiparty shared representation enabled transformer for secure medical image analysis",
  "title_text": "MST-former: multiparty shared representation enabled transformer for secure medical image analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:47Z",
  "url": "https://research.draconitic.com/paper/dq5yh8bzhvcgn",
  "venue": "Scientific Reports",
  "versions": []
}