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

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders

Computerized Medical Imaging and Graphics(2020)10.1016/j.compmedimag.2020.101733Source: DataRank Database

Healthy versus pathological learning transferability in shoulder muscle MRI segmentation using deep convolutional encoder-decoders is a research paper published in Computerized Medical Imaging and Graphics (2020). On theSindex it has a DataRank of 0. It has been cited 4 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access4 citations

Download PDF

Cite:

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 (2 grants)Impact (FWCI 0.78)OA: bronzeTopics (26)IDs (PubMed, PMC, OpenAlex)

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 (5)

Sylvain Brochard ORCID,Valérie Burdin ORCID,Frances T. Sheehan,Christelle Pons ORCID,Pierre-Henri Conze ORCID

Live enrichment

PubMed:32505943 PMC:PMC9926537 OpenAlex:https://openalex.org/W2907201784

Intramural NIH HHS

Grant: Z99 CL999999

Intramural NIH HHS

Grant: Z01 CL060062

FWCI

0.78

Citation Percentile

0.7%

Citation Trend

2020

2021

2022

bronzeLicense: cc-by-nc

journalhttps://www.sciencedirect.com/science/article/am/pii/S0895611120300367?via%3Dihub

publisherhttps://www.sciencedirect.com/science/article/am/pii/S0895611120300367?via%3Dihub

publisherhttps://api.elsevier.com/content/article/PII:S0895611120300367?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0895611120300367?httpAccept=text/plain

journalhttps://doi.org/10.1016/j.compmedimag.2020.101733

Fields of Study

Shoulder Injury and TreatmentShoulder and Clavicle InjuriesHip disorders and treatments

MeSH Terms

Neonatal Brachial Plexus PalsyFeasibility StudiesHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMuscle, Skeletal

Keywords

Rotator cuffSegmentationDeep learningComputer scienceArtificial intelligenceConvolutional neural networkDeltoid curveContext (archaeology)MedicinePattern recognition (psychology)RadiologyAnatomyObstetrical Brachial Plexus PalsyMusculo-skeletal DisordersDeep Convolutional Encoder-decodersHealthy Versus Pathological TransferabilityShoulder Muscle Segmentation

Related Papers (1)

Three-dimensional MRI Bone Models of the Glenohumeral Joint Using Deep Learning: Evaluation of Normal Anatomy and Glenoid Bone Loss

Top 15%

1.4DataRank

Radiology Artificial Intelligence(2020)

co-cited

10.1148/ryai.2020190116