ResearcherEwha Womans UniversityNo recent work listed

Thanaporn Viriyasaranon

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

Matched on ORCID iD 0000-0003-1408-9651; OpenAlex author ID A5046329058. Sources: OpenAlex.

Their work

Papers: 1
Trials: 0
Grants: 0
Labs led: 0
Collaborators: 6

Papers, trials and grants by year

2023: 1 paper

Papers 1

Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospective Diagnostic Study. Co-author DOI 10.3390/cancers15133392 ↗

Closest collaborators

The 6 strongest of 6 by collab_strength@1: shared papers, trials and grants, recent and small-team work weighing most.

Min Kyu Jung · 1 shared paper, 2023 · strength 0.18
- Paper Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospe… DOI 10.3390/cancers15133392 ↗
Seong‐Hun Kim · 1 shared paper, 2023 · strength 0.18
- Paper Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospe… DOI 10.3390/cancers15133392 ↗
Jung Won Chun · 1 shared paper, 2023 · strength 0.18
- Paper Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospe… DOI 10.3390/cancers15133392 ↗
Jae Hee Cho · 1 shared paper, 2023 · strength 0.18
- Paper Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospe… DOI 10.3390/cancers15133392 ↗
Woo Jin Lee · 1 shared paper, 2023 · strength 0.18
- Paper Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospe… DOI 10.3390/cancers15133392 ↗
Sang Myung Woo · 1 shared paper, 2023 · strength 0.18
- Paper Annotation-Efficient Deep Learning Model for Pancreatic Cancer Diagnosis and Classification Using CT Images: A Retrospe… DOI 10.3390/cancers15133392 ↗

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.