ResearcherUniversity of Nebraska–LincolnNo recent work listed

Yu Shi

ORCID-verified (high confidence, 0.95), identity_conf@1As of 8 November 2026

Matched on ORCID iD 0000-0003-1272-8392; OpenAlex author ID A5100673011. Sources: OpenAlex.

Their work

Papers: 1
Trials: 0
Grants: 0
Labs led: 0
Collaborators: 5

Papers, trials and grants by year

2023: 1 paper

Papers 1

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 P… Co-author DOI 10.3390/cancers15235496 ↗

Closest collaborators

The 5 strongest of 5 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

Chi Zhang · 1 shared paper, 2023 · strength 0.19
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
Michael Anthony Hollingsworth · 1 shared paper, 2023 · strength 0.19
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
Hongfeng Yu · 1 shared paper, 2023 · strength 0.19
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
Dandan Zheng · 1 shared paper, 2023 · strength 0.19
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗
Michael J. Baine · 1 shared paper, 2023 · strength 0.19
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496 ↗

How to reach this person

A chain of introductions through people who have published, run trials or held grants together (warm_path@1: fewest links first, then the strongest). Built from public records only; no contact details are kept.