Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification
- DOI
- 10.1016/j.media.2025.103697
- Published
- 2025-10
- Container
- Medical Image Analysis
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.media.2025.103697,
title = {Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification},
author = {Ben Philps and Maria del C. Valdés Hernández and Chen Qin and Una Clancy and Eleni Sakka and Susana Muñoz Maniega and Mark E. Bastin and Angela C.C. Jochems and Joanna M. Wardlaw and Miguel O. Bernabeu},
year = {2025},
journal = {Medical Image Analysis},
doi = {10.1016/j.media.2025.103697},
url = {https://doi.org/10.1016/j.media.2025.103697}
}RIS
TY - JOUR TI - Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification AU - Ben Philps AU - Maria del C. Valdés Hernández AU - Chen Qin AU - Una Clancy AU - Eleni Sakka AU - Susana Muñoz Maniega AU - Mark E. Bastin AU - Angela C.C. Jochems AU - Joanna M. Wardlaw AU - Miguel O. Bernabeu PY - 2025 JO - Medical Image Analysis DO - 10.1016/j.media.2025.103697 UR - https://doi.org/10.1016/j.media.2025.103697 ER -
APA
Philps, B., Hernández, M. D. C. V., Qin, C., Clancy, U., Sakka, E., Maniega, S. M., Bastin, M. E., Jochems, A. C., Wardlaw, J. M., & Bernabeu, M. O. (2025). Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification. Medical Image Analysis. https://doi.org/10.1016/j.media.2025.103697
Source records
- crossref · retrieved 2026-09-26T12:31:15.866Z