ResearcherNational Taiwan UniversityActive in the last 2 years
Po‐Ting Chen
Matched on ORCID iD 0000-0002-8675-5863; OpenAlex author ID A5102027036. Sources: OpenAlex.
Their work
- Papers
- 6 (first author 1, last author 0)
- Trials
- 0
- Grants
- 0
- Labs led
- 0
- Collaborators
- 5
2020: 1 paper · 2021: 1 paper · 2022: none · 2023: 2 papers · 2024: none · 2025: none · 2026: 2 papers
Papers 6
- Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography examinations: A multicenter retrospective … Co-author DOI 10.1016/j.diii.2026.01.007↗
- Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis. Co-author DOI 10.1093/radadv/umag017↗
- Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. Co-author DOI 10.1148/radiol.220152↗
- Detection of pancreatic cancer with two- and three-dimensional radiomic analysis in a nationwide population-based real-world dataset. First author DOI 10.1186/s12885-023-10536-8↗
- Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas. Co-author DOI 10.1148/rycan.2021210010↗
- Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Co-author DOI 10.1016/s2589-7500(20)30078-9↗
Closest collaborators
The 5 strongest of 5 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.
- Weichung Wang · 6 shared papers, 2020–2026 · strength 1.18
- Paper Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis. DOI 10.1093/radadv/umag017↗
- Paper Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography ex… DOI 10.1016/j.diii.2026.01.007↗
- Paper Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. DOI 10.1148/radiol.220152↗
- Wei‐Chih Liao · 6 shared papers, 2020–2026 · strength 1.18
- Paper Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis. DOI 10.1093/radadv/umag017↗
- Paper Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography ex… DOI 10.1016/j.diii.2026.01.007↗
- Paper Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. DOI 10.1148/radiol.220152↗
- Dawei Chang · 5 shared papers, 2021–2026 · strength 1.06
- Paper Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis. DOI 10.1093/radadv/umag017↗
- Paper Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography ex… DOI 10.1016/j.diii.2026.01.007↗
- Paper Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. DOI 10.1148/radiol.220152↗
- Kao‐Lang Liu · 5 shared papers, 2020–2026 · strength 0.89
- Paper Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis. DOI 10.1093/radadv/umag017↗
- Paper Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. DOI 10.1148/radiol.220152↗
- Paper Detection of pancreatic cancer with two- and three-dimensional radiomic analysis in a nationwide population-based real-… DOI 10.1186/s12885-023-10536-8↗
- Andre Yanchen Yeh · 3 shared papers, 2023–2026 · strength 0.75
- Paper Pancreatic cancer diagnosis on unenhanced CT with deep learning for opportunistic diagnosis. DOI 10.1093/radadv/umag017↗
- Paper Artificial intelligence for early detection of pancreatic cancer in prediagnostic and diagnostic computed tomography ex… DOI 10.1016/j.diii.2026.01.007↗
- Paper Detection of pancreatic cancer with two- and three-dimensional radiomic analysis in a nationwide population-based real-… DOI 10.1186/s12885-023-10536-8↗
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.