Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Self‐supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences

Medical Physics(2024)10.1002/mp.17541Source: DataRank Database

Self‐supervised learning improves robustness of deep learning lung tumor segmentation models to CT imaging differences is a research paper published in Medical Physics (2024). On theSindex it has a DataRank of 0. It has been cited 12 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access12 citations

Download PDF

Cite:

Linked data & code

doi

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 3.20)OA: hybridTopics (33)IDs (PubMed, PMC, OpenAlex)Annotations (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)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (3)

Aneesh Rangnekar ORCID,Harini Veeraraghavan ORCID,Jue Jiang ORCID

Live enrichment

PubMed:39636237 PMC:PMC12044619 OpenAlex:https://openalex.org/W4405071200

National Cancer Institute

Grant: P30CA008748

Norges Idrettshøgskole

Grant: R01CA258821

Memorial Sloan Kettering Cancer Center

FWCI

3.20

Citation Percentile

0.9%

Citation Trend

2025

2026

hybridLicense: public-domain

journalhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17541

publisherhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/mp.17541

publisherhttps://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.17541

publisherhttps://aapm.onlinelibrary.wiley.com/doi/full-xml/10.1002/mp.17541

journalhttps://doi.org/10.1002/mp.17541

Fields of Study

Radiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and TreatmentAdvanced Radiotherapy TechniquesDeep LearningLung NeoplasmsHumansTomography, X-Ray ComputedSupervised Machine LearningImage Processing, Computer-AssistedCarcinoma, Non-Small-Cell LungNeural Networks, Computer

MeSH Terms

Supervised Machine LearningDeep LearningCarcinoma, Non-Small-Cell LungHumansImage Processing, Computer-AssistedLung NeoplasmsTomography, X-Ray ComputedNeural Networks, ComputerImaging, Three-Dimensional

Keywords

Artificial intelligenceComputer scienceRobustness (evolution)Convolutional neural networkPattern recognition (psychology)Medical imagingDeep learningSegmentationMachine learningcomputed tomographyLung Cancer SegmentationImage Acquisition RobustnessSelf‐supervised Learning