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A deep learning based geographic attention model for body composition tissue segmentation

PubMed(2023)10.1117/12.2653371Source: DataRank Database

A deep learning based geographic attention model for body composition tissue segmentation is a research paper published in PubMed (2023). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: greenTopics (25)IDs (PubMed, PMC, OpenAlex)

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Open Access

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Reusable (0/3)

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Authors (9)

Jayaram K. Udupa ORCID,Drew A. Torigian ORCID,Yubing Tong ORCID,Pengju Nie,Jing Zhang ORCID

Live enrichment

PubMed:37261083 PMC:PMC10230574 OpenAlex:https://openalex.org/W4363650415

NCI NIH HHS

Grant: R01 CA255748

National Institutes of Health

Grant: 5R01CA255748-03

Models and methods for automatically measuring disease body-wide and staging disease via FDG-PET/CT in Lymphoma

green

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10230574/pdf/nihms-1903108.pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/10230574

http://dx.doi.org/10.1117/12.2653371

repositoryhttps://pubmed.ncbi.nlm.nih.gov/37261083

Fields of Study

Body Composition Measurement TechniquesNutrition and Health in AgingInfrared Thermography in Medicine0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

Keywords

Computer scienceSegmentationArtificial intelligenceGeneralizationPattern recognition (psychology)Composition (language)Representation (politics)Deep learningArtificial neural networkNet (polyhedron)Image segmentationBackpropagationComputer visionMathematicsDeep Neural NetworksConvolutional Neural Networks (Cnn)Fully Convolutional NetworksBody Composition Analysis (Bca)Body Composition SegmentationBody Tissue Segmentation