{
  "abstract": "Spatial transcriptomics jointly captures molecular state, tissue morphology, and spatial organization, yet spatial-domain methods are typically compared only at late training checkpoints, obscuring differences in convergence efficiency. We introduce MP-MNCA, a morphology-prior masked neighbor cross-attention framework that preserves the full 3,000 dimensional highly-variable-gene representation while selectively routing molecular information across spatial neighbors, with histological similarity incorporated as a continuous attention prior under a self-supervised masked neighbor-contrastive objective. Across 12 heterogeneous human dorsolateral prefrontal cortex (DLPFC) sections, MP-MNCA is evaluated against established spatial-domain baselines using both convergence trajectories and epoch-wise spatial-domain visualizations. Our results show that MP-MNCA (i) reaches coherent spatial-domain structure in substantially fewer training epochs, as reflected quantitatively by convergence curves and qualitatively by progressive spatial grids, and (ii) maintains strong clustering quality, achieving a mean peak refined ARI of 0.546 versus 0.549 for the strongest baseline (STAIG), while attaining the highest mean refined NMI of 0.657 versus 0.640. Moreover, MP-MNCA outperforms STAIG on 7 of 12 sections in ARI and 11 of 12 in NMI, demonstrating faster convergence without sacrificing spatial-domain quality.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.16212",
  "author_total": 2,
  "authors": [
    {
      "name": "Sidharthan Sivacoumar Chitra"
    },
    {
      "name": "Sanjeev Manivannan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3tbsrj4wg954g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.16212",
  "fulltext_url": null,
  "id": "3tbsrj4wg954g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.16212",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.TO"
    ],
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7221756943",
  "pdf_url": "https://arxiv.org/pdf/2610.16212",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/3tbsrj4wg954g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:21Z",
      "native_id": "2610.16212",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.16212"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "arxiv:q-bio.TO",
      "subfield": "physiology"
    }
  ],
  "title": "MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification",
  "title_text": "MP-MNCA: Morphology-Prior Masked Neighbor Cross-Attention for Efficient Spatial Domain Identification",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:21Z",
  "url": "https://research.draconitic.com/paper/3tbsrj4wg954g",
  "venue": "arXiv",
  "versions": []
}