{
  "abstract": "This study uses methodological auditing to evaluate a proof-of-concept engineering pipeline for classifying radiological severity groups of preterm brain injury (BI) from T2-weighted MRI in the dHCP cohort. Its primary contribution is methodology-driven empirical discovery rather than a new neural-network architecture. GloResNet combines a standard ResNet-10 backbone, MedicalNet initialization, whole-volume array resizing, mixup, and array-axis-0-reversal probability averaging, hereafter referred to as axis-0-reversal averaging; no new CNN block is proposed. The audit identifies concrete mechanisms by which small-cohort imaging results can be overstated, including unit-of-analysis drift, fold-assignment non-traceability, post hoc model-selection bias, and ranking across incompatible evaluation protocols. Building on this framework, we report the first fair, traceable comparison between a de novo trained Vision Transformer (ViT) and a domain-adapted lightweight 3D CNN on a public preterm brain injury dataset. When all sources of bias are controlled, the lightweight CNN achieves comparable or superior performance to ViT, challenging the prevailing assumption that larger, more complex architectures are always preferable in low-data regimes. Applying the audit to the historical evidence also showed that the archived GloResNet mean fold accuracy of $$69.12 \\pm 7.42\\%$$ was calculated over 180 scan records from 128 subjects, including repeated scans, without retained subject identifiers or out-of-fold predictions. The historical archive is therefore reported separately from the fair, traceable server-side comparison. A separate, auditable subject-level input-representation experiment did not demonstrate superiority of whole-volume input. The resulting minimum reporting standard requires an explicit statistical unit, traceable subject-level folds, retained out-of-fold predictions, prespecified selection rules, and transparent comparator conditions. These internal estimates do not establish clinical screening performance or external generalizability. The source code is available at https://github.com/ICL-SUST/GloResNet-Preterm-Brain",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "Boyu Yuan"
    },
    {
      "name": "Jiamiao Lu"
    },
    {
      "name": "Weichuan Zhang"
    },
    {
      "name": "Benqing Wu"
    },
    {
      "name": "Tuo Wang"
    },
    {
      "name": "Changshan Wang"
    },
    {
      "name": "Changming Sun"
    },
    {
      "name": "Liang Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bsy9x7gy8htj9.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-77358-z",
  "fulltext_url": null,
  "id": "bsy9x7gy8htj9",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-77358-z",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2735|Neonatal and fetal brain pathology"
    ]
  ],
  "openalex": "W7223411245",
  "pdf_url": "https://www.nature.com/articles/s41598-026-77358-z_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/bsy9x7gy8htj9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:33Z",
      "native_id": "10.1038/s41598-026-77358-z",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-77358-z"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2735|Neonatal and fetal brain pathology",
      "subfield": "reproductive-child"
    }
  ],
  "title": "Methodological audit and empirical evaluation of a lightweight whole-volume 3D CNN for preterm brain injury classification",
  "title_text": "Methodological audit and empirical evaluation of a lightweight whole-volume 3D CNN for preterm brain injury classification",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:33Z",
  "url": "https://research.draconitic.com/paper/bsy9x7gy8htj9",
  "venue": "Scientific Reports",
  "versions": []
}