{
  "abstract": "Counterfactual medical images are useful only when a requested clinical change is introduced without silently altering patient characteristics that should remain fixed. Existing diffusion editors can produce convincing images, but realism or target-classifier flips do not establish that an edit is localized, diagnosis-specific, anatomically faithful, or useful on real clinical data. We introduce PATHEDIT, a state-factorized counterfactual editing framework that decomposes an abdominal CT representation into pathology, anatomy, acquisition, and residual context before performing a clinically specified latent intervention. A pathology editor modifies only the target factor, while non-target factors are explicitly copied and preservation losses penalize collateral drift. The edited state is decoded jointly into an image, a report level description, and diagnostic readouts. Weak supervision from lesion masks, organ segmentations, and naturally occurring longitudinal lesion changes anchors edit direction and spatial support without requiring pixel-aligned counterfactual ground truth. We evaluate four properties that are often conflated in medical image editing: target edit success, non-target preservation, anatomical identity, and real-data utility. In our evaluation protocol, PATHEDIT achieves 94.2% target success while reducing mean off-target diagnostic change to 3.0%, yields substantially stronger alignment with observed lesion changes, and improves rare-lesion recognition and shortcut robustness on held-out real CT. These results motivate astricter view of counterfactual medical imaging: a useful edit must prove not only what changed, but also what did not.",
  "abstract_shown": true,
  "affiliations": [
    "Zhengzhou University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "J. Chen"
    },
    {
      "affs": [
        0
      ],
      "name": "W. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/f9pa0b7tgwqvs.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.21.753387",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753387v1.full",
  "id": "f9pa0b7tgwqvs",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753387v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biochemistry"
    ],
    [
      "openalex",
      "1707|Generative Adversarial Networks and Image Synthesis"
    ]
  ],
  "openalex": "W7214016721",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753387v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-21",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/f9pa0b7tgwqvs.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:57Z",
      "native_id": "10.64898/2026.09.21.753387",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.21.753387v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biochemistry",
      "subfield": "general"
    }
  ],
  "title": "PathEdit: Diagnostic-Preserving Counterfactual Medical Image Editing through State-Factorized Generative Intervention",
  "title_text": "PathEdit: Diagnostic-Preserving Counterfactual Medical Image Editing through State-Factorized Generative Intervention",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:57Z",
  "url": "https://research.draconitic.com/paper/f9pa0b7tgwqvs",
  "venue": "bioRxiv",
  "versions": []
}