{
  "abstract": "Molecular assays such as spatial transcriptomics facilitate fine-grained identification of cell types for analyzing tumor microenvironments. However, they are expensive to acquire in routine clinical practice compared to hematoxylin and eosin (H&E) images. With molecular guidance, such images can distinguish cell lineages, yet often fail to resolve closely related subtypes. To overcome this, we introduce CellBridge, a multi-modal contrastive learning framework pretrained on 16M cells with paired H&E and spatial profiles, for transferring molecular information into morphological representations. CellBridge further incorporates a Cell-Ontology-aware conformal prediction module that assigns labels at different levels of specificity: leaf-level cell types when morphology alone provides sufficient evidence, and broader ancestor categories when distinguishing among more specific cell types would require molecular information. Across in-domain and out-of-domain benchmarks, CellBridge outperforms leading pathology foundation models in macro F1 by up to 16 percentage points, with the largest gains on the most fine-grained label spaces. It consistently achieves smaller prediction sets, while reducing class-wise coverage gaps. We envision CellBridge as a step toward extracting cellular information that currently requires costly molecular assays from routinely acquired pathology images, while explicitly identifying where such assays remain necessary. Our code is available at https://github.com/Lotfollahi-lab/cellbridge.",
  "abstract_shown": true,
  "affiliations": [
    "EPFL"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 12,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "S. Ogut"
    },
    {
      "name": "A. Merchant"
    },
    {
      "name": "A. Vahidi"
    },
    {
      "name": "D. Jeong"
    },
    {
      "name": "S. Birk"
    },
    {
      "name": "A. Boxall"
    },
    {
      "name": "A. Miraki Feriz"
    },
    {
      "name": "M. R. Sallese"
    },
    {
      "name": "Y. Chang"
    },
    {
      "name": "P. Frossard"
    },
    {
      "name": "D. Thanou"
    },
    {
      "name": "M. Lotfollahi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/44m3r8zv9rnq7.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.20.760347",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760347v1.full",
  "id": "44m3r8zv9rnq7",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760347v1",
  "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": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1702|AI in cancer detection"
    ]
  ],
  "openalex": "W7223552710",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760347v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/44m3r8zv9rnq7.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:34Z",
      "native_id": "10.64898/2026.10.20.760347",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.20.760347v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Fine-Grained and Reliable Cell Classification from Pathology Images",
  "title_text": "Fine-Grained and Reliable Cell Classification from Pathology Images",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:34Z",
  "url": "https://research.draconitic.com/paper/44m3r8zv9rnq7",
  "venue": "bioRxiv",
  "versions": []
}