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Unsupervised Domain Adaptation for Small Bowel Segmentation Using Disentangled Representation

Lecture notes in computer science(2021)10.1007/978-3-030-87199-4_27Source: DataRank Database

Unsupervised Domain Adaptation for Small Bowel Segmentation Using Disentangled Representation is a research paper published in Lecture notes in computer science (2021). On theSindex it has a DataRank of 0. It has been cited 2 times.

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0DataRank · unranked

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Open Access2 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (1 grants)Impact (FWCI 0.49)OA: greenTopics (23)IDs (PubMed, PMC, OpenAlex)SDGs (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)

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Authors (3)

Sungwon Lee ORCID,Ronald M. Summers ORCID,Seung Yeon Shin ORCID

Live enrichment

PubMed:35601480 PMC:PMC9115845 OpenAlex:https://openalex.org/W3179427844

Intramural NIH HHS

Grant: Z01 CL040004

FWCI

0.49

Citation Percentile

0.7%

Citation Trend

2022

2025

greenLicense: https://www.springer.com/tdm

repositoryhttps://arxiv.org/pdf/2107.02854

GREENhttps://arxiv.org/pdf/2107.02854

repositoryhttps://arxiv.org/pdf/2107.02854

publisherhttps://link.springer.com/content/pdf/10.1007/978-3-030-87199-4_27

repositoryhttp://arxiv.org/abs/2107.02854

Fields of Study

Colorectal Cancer Screening and DetectionCOVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingMedicineEngineeringComputer Science

Keywords

SegmentationComputer scienceAdaptation (eye)Artificial intelligenceFeature (linguistics)Pattern recognition (psychology)Domain adaptationRepresentation (politics)Contrast (vision)Domain (mathematical analysis)Encoding (memory)MathematicsPsychologyAbdominal Computed TomographyUnsupervised Domain AdaptationSmall Bowel SegmentationFeature Disentanglement

Sustainable Development Goals

SDG 0: Reduced inequalities