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Siam-VAE: a hybrid deep learning based anomaly detection framework for automated quality control of head CT scans

PubMed(2023)10.1117/12.2654464Source: DataRank Database

Siam-VAE: a hybrid deep learning based anomaly detection framework for automated quality control of head CT scans is a research paper published in PubMed (2023). On theSindex it has a DataRank of 0. It has been cited 1 time.

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0DataRank · unranked

Scored on demand from live citation data

Open Access1 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (5 grants)OA: greenTopics (16)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

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Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (4)

Rajat Dhar ORCID,Daniel S. Marcus ORCID,Aristeidis Sotiras ORCID,Soumyendu Sekhar Ghosh ORCID

Live enrichment

PubMed:39040978 PMC:PMC11262463 OpenAlex:https://openalex.org/W4362663626

NIH HHS

Grant: S10 OD018091

NCRR NIH HHS

Grant: S10 RR022984

NINDS NIH HHS

Grant: P30 NS098577

NIH HHS

Grant: S10 OD025200

NINDS NIH HHS

Grant: K23 NS099440

green

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11262463/pdf/nihms-2009037.pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/11262463

http://dx.doi.org/10.1117/12.2654464

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39040978

Fields of Study

Anomaly Detection Techniques and ApplicationsMedical Imaging Techniques and ApplicationsMachine Learning in Healthcare

Keywords

Anomaly detectionComputer scienceArtificial intelligenceQuality (philosophy)Head (geology)Computer visionDeep learningGeologyPhysicsQuality controlCTSiamese Neural NetworkVariational Autoencoder