ResearcherUniversity of Nebraska–LincolnActive in the last 2 years
Hongfeng Yu
Matched on ORCID iD 0000-0002-0596-8227; OpenAlex author ID A5020745360. Sources: OpenAlex.
Their work
- Papers
- 3 (first author 0, last author 1)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 11
2022: 1 paper · 2023: 1 paper · 2024: 1 paper
Papers 3
- radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenotype in pancreatic cancer. Co-author DOI 10.1038/s41598-024-62741-5↗
- 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… Last author DOI 10.3390/cancers15235496↗
- Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Co-author DOI 10.3390/cancers14071654↗
Closest collaborators
The 11 strongest of 11 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Michael Anthony Hollingsworth · 3 shared papers, 2022–2024 · strength 0.52
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Dandan Zheng · 3 shared papers, 2022–2024 · strength 0.52
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Michael J. Baine · 3 shared papers, 2022–2024 · strength 0.52
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Chi Zhang · 2 shared papers, 2023–2024 · strength 0.37
- Paper 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synt… DOI 10.3390/cancers15235496↗
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Kiersten Preuss · 2 shared papers, 2022–2024 · strength 0.33
- Paper radioGWAS links radiome to genome to discover driver genes with somatic mutations for heterogeneous tumor image phenoty… DOI 10.1038/s41598-024-62741-5↗
- Paper Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Appl… DOI 10.3390/cancers14071654↗
- Yu Shi · 1 shared paper, 2023 · strength 0.19
- Yu Shi · 1 shared paper, 2024 · strength 0.18
- Paul M. Grandgenett · 1 shared paper, 2024 · strength 0.18
- Jeffrey Wong · 1 shared paper, 2024 · strength 0.18
- Justin A. Chen · 1 shared paper, 2022 · strength 0.15
- Chi H. Lin · 1 shared paper, 2022 · strength 0.15
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.