{
  "abstract": "Background\n\nReport-mined chest radiograph datasets commonly contain both long-tailed label distributions and uncertain annotations. Conventional frequency-based re-weighting can strengthen rare-label learning, but it may also amplify unreliable supervision when uncertain labels occur in minority classes.Purpose\n\nTo develop and evaluate an uncertainty-suppressed long-tailed learning method with lightweight feature adaptation for multi-label chest X-ray (CXR) classification under imbalanced and uncertain report-derived supervision.Methods\n\nThe proposed uncertainty-suppressed long-tailed feature adaptation (USLFA) method combines a lightweight label-adaptive feature head with an uncertainty-suppressed long-tailed loss. The primary analysis used a patient-level 70%/10%/20% split of CheXpert for 14 observation labels. Robustness was further examined across two additional patient-level partitions, on the recommended MIMIC-CXR-JPG test split without retraining, and on the official radiologist-annotated CheXpert test set. Performance was assessed using macro-AUROC, macro-AUPRC, macro F1, frequency-group analysis, ablation studies, and uncertainty-handling comparisons. Ninety-five percent confidence intervals (CIs) were estimated by patient-level bootstrap resampling, and formal pairwise tests in the primary CheXpert analysis used paired permutation testing with Holm correction.Results\n\nOn the primary CheXpert split, USLFA achieved a macro-AUROC of 0.864 (95% CI, 0.856-0.871), macro-AUPRC of 0.547 (95% CI, 0.532-0.560), and macro F1 of 0.501 (95% CI, 0.487-0.514). Compared with ConvNeXt-Tiny using binary cross-entropy (BCE), the gains in macro-AUPRC and macro F1 were both 0.051 and remained significant after Holm correction (both p < 0.001). Compared with label-distribution-aware margin (LDAM), USLFA improved macro-AUPRC by 0.018 (Holm-adjusted p = 0.024) and macro F1 by 0.017 (Holm-adjusted p = 0.041). The ordering was preserved across three patient-level partitions, with USLFA macro-AUPRC ranging from 0.540 to 0.555. On MIMIC-CXR-JPG, USLFA achieved a macro-AUPRC of 0.485 (95% CI, 0.467-0.502), and on the official CheXpert test set it achieved a mean AUROC of 0.920 (95% CI, 0.908-0.931) across the five competition observations.Conclusions\n\nUSLFA improved macro-AUPRC and rare-label recognition without introducing an additional inference-time branch. Macro F1 was significantly higher than the principal BCE and LDAM baselines in the main comparison, whereas the additional macro F1 gain attributable to uncertainty suppression over U Soft was not significant after Holm correction. The repeated-split, external-dataset, and official-test analyses provide complementary evidence that the observed advantage is not confined to a single custom partition.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Radiology, Hubei Province Academy of Traditional Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine (Guanggu Campus), Wuhan, Hubei Province, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 1,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Lu Li",
      "orcid": "0009-0003-6611-7711"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0h2b91ber0087.bib",
  "cited_by": 0,
  "doi": "10.1002/acm2.70873",
  "fulltext_url": "https://europepmc.org/articles/PMC13682625",
  "id": "0h2b91ber0087",
  "issns": [
    "1526-9914"
  ],
  "landing_url": "https://europepmc.org/article/MED/42900549",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|COVID-19 diagnosis using AI"
    ]
  ],
  "openalex": "W7223068672",
  "pdf_url": "https://europepmc.org/articles/PMC13682625?pdf=render",
  "pmcid": "PMC13682625",
  "pmid": "42900549",
  "published": "2026-10-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/0h2b91ber0087.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:32Z",
      "native_id": "MED:42900549",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42900549"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|COVID-19 diagnosis using AI",
      "subfield": "imaging"
    }
  ],
  "title": "Uncertainty-suppressed long-tailed learning with lightweight feature adaptation for multi-label chest X-ray classification.",
  "title_text": "Uncertainty-suppressed long-tailed learning with lightweight feature adaptation for multi-label chest X-ray classification.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:32Z",
  "url": "https://research.draconitic.com/paper/0h2b91ber0087",
  "venue": "Journal of applied clinical medical physics",
  "versions": []
}