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Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models

Journal of Pathology Informatics(2025)10.1016/j.jpi.2025.100443Source: DataRank Database

Efficient merging and validation of deep learning-based nuclei segmentations in H&E slides from multiple models is a research paper published in Journal of Pathology Informatics (2025). On theSindex it has a DataRank of 0. It has been cited 1 time.

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Open Access1 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.65)OA: goldTopics (11)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Shannon K. McDonnell ORCID,Zachary C. Fogarty ORCID,Nicholas B. Larson ORCID,Jagadheshwar Balan ORCID

Live enrichment

PubMed:40463413 PMC:PMC12130990 OpenAlex:https://openalex.org/W4409451838

US Department of Defense

Grant: W81XWH-11-1-0261

National Institutes of Health

Grant: CA151254

National Institutes of Health

Grant: CA157881

National Institutes of Health

Grant: CA015083

FWCI

0.65

Citation Percentile

0.6%

Citation Trend

2025

goldLicense: cc-by

journalhttps://doi.org/10.1016/j.jpi.2025.100443

publisherhttps://doi.org/10.1016/j.jpi.2025.100443

publisherhttps://api.elsevier.com/content/article/PII:S2153353925000288?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2153353925000288?httpAccept=text/plain

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40463413

Fields of Study

Radiomics and Machine Learning in Medical Imaging

Keywords

Computer scienceArtificial intelligenceDeep learningData miningGene ExpressionDigital PathologyNuclei SegmentationMultiomicsDeep-learningCell Type Proportions

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure