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G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation

International Journal of Computer Assisted Radiology and Surgery(2025)10.1007/s11548-024-03319-4Source: DataRank Database

G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation is a research paper published in International Journal of Computer Assisted Radiology and Surgery (2025). On theSindex it has a DataRank of 0. It has been cited 2 times.

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2 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (5 grants)Impact (FWCI 1.00)OA: closedTopics (45)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Asem Ali ORCID,Mohamed Yousuf,Salwa Elshazly,Aly A. Farag,Samir Harb ORCID

Live enrichment

PubMed:39789205 OpenAlex:https://openalex.org/W4406202479

National Science Foundation

Grant: 1602333 and 2124316

National Institutes of Health

Grant: 1R43CA179911-01 and 1R43CA250750-01

National Institutes of Health

Grant: 1R43CA250750-01

Robust Visualization in CT Colonography

National Institutes of Health

Grant: 1R43CA179911-01

Virtual Fly-Over Colonoscopy System for Clinical Test-SBIR Phase 1

National Science Foundation

Grant: 2124316

SCH: Robust CT Colonography for Local & Cloud-Based Screening

FWCI

1.00

Citation Percentile

0.7%

Citation Trend

2025

closedLicense: Springer Nature TDM

publisherhttps://link.springer.com/content/pdf/10.1007/s11548-024-03319-4.pdf

publisherhttps://link.springer.com/article/10.1007/s11548-024-03319-4/fulltext.html

journalhttps://doi.org/10.1007/s11548-024-03319-4

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39789205

Fields of Study

Colorectal Cancer Screening and DetectionCOVID-19 diagnosis using AIAI in cancer detection0202 electrical engineering, electronic engineering, information engineering02 engineering and technologyHumansDeep LearningColonography, Computed TomographicAlgorithmsReproducibility of ResultsColonic PolypsColonRadiographic Image Interpretation, Computer-AssistedImaging, Three-Dimensional

MeSH Terms

Deep LearningAlgorithmsColonColonic PolypsHumansMarkov ChainsRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsImaging, Three-DimensionalColonography, Computed Tomographic

Keywords

Computer scienceArtificial intelligenceSegmentationSørensen–Dice coefficientPattern recognition (psychology)Robustness (evolution)AnnotationDeep learningImage segmentationMachine learningCTCMedical Image SegmentationFew-shotImaging, Three-DimensionalColonHumansReproducibility of ResultsColonic PolypsRadiographic Image Interpretation, Computer-AssistedColonography, Computed TomographicAlgorithms

G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation

FAIR Checklist

Authors (5)

Funding (5 grants)

Impact

Open Access

Topics