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

Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN

Methods(2022)10.1016/j.ymeth.2022.07.007Source: DataRank Database

Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN is a research paper published in Methods (2022). On theSindex it has a DataRank of 0. It has been cited 15 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access15 citations

Free full text

Cite:

Linked data & code

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)OA: greenTopics (25)Linked Datasets (2)DataCite Reuse (2)IDs (PubMed)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

DataCite relations
Linked datasets

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 (15)

Yi Xin ORCID,Sarah E. Gerard ORCID,Jacob Herrmann ORCID,Parth Shah ORCID,Kevin Martin

Live enrichment

PubMed:35817338

National Institutes of Health

Grant: 1R01HL137389-01A1

An integrated approach to predict and improve the outcomes of lung injury

NHLBI NIH HHS

Grant: R01 HL137389

greenLicense: cc-by-nc-nd

repositoryhttps://www.sciencedirect.com/science/article/pii/S104620232200161X?via=ihub

publisherhttps://api.elsevier.com/content/article/PII:S104620232200161X?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S104620232200161X?httpAccept=text/plain

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9288584

https://doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a4469

Fields of Study

03 medical and health sciences0302 clinical medicine0202 electrical engineering, electronic engineering, information engineering02 engineering and technologyCOVID-19HumansImage Processing, Computer-AssistedLungTomography, X-Ray Computed

MeSH Terms

COVID-19HumansImage Processing, Computer-AssistedLungTomography, X-Ray Computed

Keywords

COVID-19; CT; CycleGAN; Lesion segmentation;Image Processing, Computer-AssistedCOVID-19HumansCOVID-19; CT; CycleGAN; Lesion segmentationTomography, X-Ray ComputedLungArticleCTCycleGANLesion segmentation

Sustainable Development Goals

SDG 3: 3. Good health

Additional file 1 of Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan

10.6084/m9.figshare.26500347.v1figshare

Additional file 1 of Phenotyping COVID-19 respiratory failure in spontaneously breathing patients with AI on lung CT-scan

10.6084/m9.figshare.26500347figshare