{
  "id": 10786732,
  "title": "Beyond Marginal Coverage: Class-Conditional Conformal Prediction in Multi-Class Psychiatric Neuroimaging",
  "url": "https://urgent.news/2026/09/29/beyond-marginal-coverage-class-conditional-conformal-prediction-in",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-29T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753958v1?rss=1"
  },
  "original_language": "en",
  "account": "Conformal prediction is gaining traction for clinical decision support due to its ability to offer distribution-free coverage guarantees applicable to any model and data distribution. However, the commonly reported coverage guarantee is marginal, and the study reveals that in multi-class diagnostic classification, it can be met exactly while individual diagnoses experience highly uneven coverage. Utilizing a four-way mood and psychosis classification task involving 1,520 subjects from three studies and 14 acquisition sites, the research demonstrates that marginal split-conformal calibration achieved an empirical coverage of 0.9000, compared to the target of 0.90. Yet, coverage varied significantly between groups - healthy controls at 0.941 and schizoaffective disorder at 0.818. The disparity between these figures is neutralized, rendering the overall result inconclusive.\n\nThe study further explores class-conditional (Mondrian) calibration, which mitigates the disparity in coverage by a mere 12.3 points, resulting in a minimal 0.4-point difference. However, this improvement comes at the expense of a 0.07 label reduction in mean set size. An intriguing consequence of maintaining consistent coverage across diagnoses is the inability to attribute residual variation in set size to class prior or per-class accuracy. Instead, this variation becomes a distinct property of the subject. The prediction sets thus classify subjects into four categories: confident, boundary, ambiguous, and unresolved. The proportion of subjects flagged as label-ambiguous by a structural-MRI model trained separately on the same cohort increases gradually across these strata (34.1%, 57.3%, 68.1%, 81.8%; p = 8.8e-18). Notably, no schizoaffective subject and only 0.9% of bipolar subjects reach the confident stratum, while 18.0% of controls and 13.4% of schizophrenia subjects do. Furthermore, set size at matched coverage provides a means to compare representations that accuracy alone cannot. When applied to structural MRI, functional MRI, and their fusion, the findings reveal a reversal in sign that accuracy fails to capture: fusion reduces set size for bipolar and schizoaffective subjects, while increasing it for controls. The aggregate benefit of fusion changes sign below alpha = 0.10, while top-1 accuracy fluctuates from 0.495 to 0.578 to 0.611 across the three models. The researchers caution that reporting marginal coverage alone is insufficient when diagnostic classes are unbalanced.",
  "summary": "Conformal prediction is increasingly proposed for clinical decision support because it provides distribution-free coverage guarantees that hold for any model and any data distribution. The guarantee routinely reported, however, is marginal, and we show that in multi-class diagnostic classification it can be satisfied exactly while individual diagnoses are covered very unevenly. On a four-way mood…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}