{
  "abstract": "Computational pathology models infer biologically and clinically meaningful outcomes from histology, but their predictions are shaped by complex, intertwined tissue signals whose roles are important to understand. Common pixel- and feature-space perturbations can produce implausible tissue, making model responses difficult to interpret. We introduce CPathOGen, a conditional latent-diffusion framework for generating paired H\\&E counterfactuals with explicit controls over cellular spatial organization, nuclear morphology, and stain appearance. Cellular maps condition spatial structure through a spatial encoder, while a morphology/appearance vector modulates denoising through blockwise feature-wise linear modulation (FiLM). On held-out H\\&E tiles, CPathOGen generates visually plausible tissue with improved distributional agreement after spatially guided selection, as reflected by lower Fréchet Inception Distance (FID) and Kernel Inception Distance (KID); generated cells track requested abundance and position, and measured morphology and color vary monotonically with their controls. We use these verified interventions to probe pathology encoders with endpoint heads, task-specific classifiers, and survival models. Responses are quantified using total variation distance and prediction-flip rate. We further introduce the Biology-Nuisance Sensitivity Ratio, a metric that contrasts model sensitivity to biologically motivated morphology and spatial factors with sensitivity to non-biological, stain-related nuisance variation. CPathOGen provides a practical, fidelity-audited framework for evaluating robustness and controlled feature sensitivity in computational pathology model. \\href{https://github.com/a12dongithub/PathOGen}{GitHub} and \\href{https://huggingface.co/a12donhf/CPathOGen}{Hugging~Face}.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.00602",
  "author_total": 2,
  "authors": [
    {
      "name": "Samarth Singhal"
    },
    {
      "name": "Varang Rai"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8rk1c5vz8wak8.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.00602",
  "fulltext_url": null,
  "id": "8rk1c5vz8wak8",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.00602",
  "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",
      "eess.IV"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.00602",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8rk1c5vz8wak8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:08Z",
      "native_id": "2610.00602",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.00602"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "CPathOGen: Spatially and Morphologically Controlled H&E Counterfactuals for Probing Pathology Models",
  "title_text": "CPathOGen: Spatially and Morphologically Controlled H&E Counterfactuals for Probing Pathology Models",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:08Z",
  "url": "https://research.draconitic.com/paper/8rk1c5vz8wak8",
  "venue": "arXiv",
  "versions": []
}