{
  "abstract": "Label-free virtual staining offers a compelling, non-destructive alternative to standard histopathology; however, its clinical adoption is hindered by the computational bottlenecks inherent to processing gigapixel Whole Slide Images (WSIs). Current deep learning approaches require patch-based inference to avoid memory constraints, which disrupts global tissue continuity and introduces tiling artifacts--displaying visible seams and color shifts. To address this, we introduce the Consistency Memory Bank (COMB), a novel label-free virtual staining framework that enforces spatial and channel consistency across tiles without memory bottlenecks. COMB decouples context storage from computation, utilizing a dynamic retrieval mechanism to fetch feature representations from adjacent tiles. This enables a retrieval-based context integration strategy that adopts local padding to resolve spatial discontinuities and neighbor-aware channel attention to stabilize statistical drift. Further optimized with a sliding window schedule to ensure minimal memory overhead, our method demonstrates superior performance over state-of-the-art baselines, achieving significant improvements in both perceptual fidelity and tiling consistency, while suggesting its downstream utility in tumor segmentation. Code is available at https://github.com/dou0000/COMB.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.10914",
  "author_total": 6,
  "authors": [
    {
      "name": "Dou Hoon Kwark"
    },
    {
      "name": "Kianoush Falahkheirkhah"
    },
    {
      "name": "Ji-hun Oh"
    },
    {
      "name": "Shirui Luo"
    },
    {
      "name": "Volodymyr Kindratenko"
    },
    {
      "name": "Rohit Bhargava"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cp9vgw2xhx28r.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.10914",
  "fulltext_url": null,
  "id": "cp9vgw2xhx28r",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.10914",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CV"
    ],
    [
      "arxiv",
      "eess.IV"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.10914",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cp9vgw2xhx28r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:31Z",
      "native_id": "2609.10914",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.10914"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Seamless Whole Slide Label-Free Virtual Staining",
  "title_text": "Seamless Whole Slide Label-Free Virtual Staining",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:31Z",
  "url": "https://research.draconitic.com/paper/cp9vgw2xhx28r",
  "venue": "arXiv",
  "versions": []
}