Quantifying the Efficacy of Deep Learning-Driven Deformable Registration in Multiplexed-Immunofluorescence Imaging for Nucleus Subtype Classification.
- DOI
- 10.59275/j.melba.2026-912a
- Published
- 2026 Apr
- Container
- The journal of machine learning for biomedical imaging
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- 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.
- supportingOpen access status: Normalized open-access status: open.
- 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.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.59275/j.melba.2026-912a,
title = {Quantifying the Efficacy of Deep Learning-Driven Deformable Registration in Multiplexed-Immunofluorescence Imaging for Nucleus Subtype Classification.},
author = {Rudravaram G and Bao S and Remedios LW and Krishnan AR and Kim ME and Liu Y and Gao C and Zhang R and Jiang B and Liu Q and Lau KS and Roland JT and Washington MK and Coburn LA and Wilson KT and Huo Y and Landman BA},
year = {2026},
journal = {The journal of machine learning for biomedical imaging},
doi = {10.59275/j.melba.2026-912a},
url = {https://doi.org/10.59275/j.melba.2026-912a}
}RIS
TY - JOUR TI - Quantifying the Efficacy of Deep Learning-Driven Deformable Registration in Multiplexed-Immunofluorescence Imaging for Nucleus Subtype Classification. AU - Rudravaram G AU - Bao S AU - Remedios LW AU - Krishnan AR AU - Kim ME AU - Liu Y AU - Gao C AU - Zhang R AU - Jiang B AU - Liu Q AU - Lau KS AU - Roland JT AU - Washington MK AU - Coburn LA AU - Wilson KT AU - Huo Y AU - Landman BA PY - 2026 JO - The journal of machine learning for biomedical imaging DO - 10.59275/j.melba.2026-912a UR - https://doi.org/10.59275/j.melba.2026-912a ER -
APA
G, R., S, B., LW, R., AR, K., ME, K., Y, L., C, G., R, Z., B, J., Q, L., KS, L., JT, R., MK, W., LA, C., KT, W., Y, H., & BA, L. (2026). Quantifying the Efficacy of Deep Learning-Driven Deformable Registration in Multiplexed-Immunofluorescence Imaging for Nucleus Subtype Classification.. The journal of machine learning for biomedical imaging. https://doi.org/10.59275/j.melba.2026-912a
Source records
- pubmed · retrieved 2026-09-25T08:53:17.194Z