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

3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer

Cancers(2023)10.3390/cancers15235496Source: DataRank Database

3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer is a research paper published in Cancers (2023). On theSindex it has a DataRank of 0. It has been cited 17 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access17 citations

Download PDF

Cite:

Linked data & code

doi

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 (7 grants)Impact (FWCI 2.22)OA: goldTopics (30)IDs (PubMed, PMC, OpenAlex)Annotations (1)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)

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 (8)

Hannah Tang,Michael J. Baine ORCID,Michael A. Hollingsworth ORCID,Huijing Du,Dandan Zheng ORCID

Live enrichment

PubMed:38067200 PMC:PMC10705188 OpenAlex:https://openalex.org/W4388864626

Nebraska Collaboration Initiative

Grant: 19-20

Nebraska Collaboration Initiative

Grant: 20-21

Nebraska Collaboration Initiative

Grant: 5U54GM115458-03

Great Plains IDeA-CTR

Nebraska Collaboration Initiative

Grant: DMS-1853636

NIGMS NIH HHS

Grant: U54 GM115458

National Science Foundation

Grant: 1853636

Mathematical Modeling of Robust Spatiotemporal Dynamics in Epidermal Development

Layman Fund held at the University of Nebraska Foundation

FWCI

2.22

Citation Percentile

0.9%

Citation Trend

2023

2024

2025

2026

goldLicense: cc-by

journalhttps://www.mdpi.com/2072-6694/15/23/5496/pdf?version=1700548449

publisherhttps://www.mdpi.com/2072-6694/15/23/5496/pdf?version=1700548449

publisherhttps://www.mdpi.com/2072-6694/15/23/5496/pdf

journalhttps://doi.org/10.3390/cancers15235496

repositoryhttps://dx.doi.org/10.3390/cancers15235496

Fields of Study

AI in cancer detectionGenerative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

Computer scienceDeep learningArtificial intelligenceSegmentationConvolutional neural networkGenerator (circuit theory)Pancreatic cancerPancreatic ductal adenocarcinomaMachine learningPattern recognition (psychology)CancerMedicineMedical imagingGenerative Adversarial Network3d Volume SynthesisClinical TumorFOS: Computer and information sciencesComputer SciencesComputer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)Computer Science - Computer Vision and Pattern Recognition610Electrical Engineering and Systems Science - Image and Video ProcessingArticleFOS: Electrical engineering, electronic engineering, information engineering

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure

3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer

FAIR Checklist

Authors (8)

Funding (7 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)