ResearcherGyeongsang National University HospitalActive in the last 2 years

Seok Jin Hong

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

Matched on ORCID iD 0000-0002-5301-6140; OpenAlex author ID A5100709913. Sources: OpenAlex.

Their work

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

Papers, trials and grants by year

2026: 1 paper

Papers 1

Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does improved image quality translate to bette… Co-author DOI 10.1007/s00261-025-05271-6 ↗

Closest collaborators

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

Jeongin Yoo · 1 shared paper, 2026 · strength 0.32
- Paper Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does i… DOI 10.1007/s00261-025-05271-6 ↗
Hyo‐Jin Kang · 1 shared paper, 2026 · strength 0.32
- Paper Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does i… DOI 10.1007/s00261-025-05271-6 ↗
Sun Kyung Jeon · 1 shared paper, 2026 · strength 0.32
- Paper Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does i… DOI 10.1007/s00261-025-05271-6 ↗
Jeong Hee Yoon · 1 shared paper, 2026 · strength 0.32
- Paper Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does i… DOI 10.1007/s00261-025-05271-6 ↗
Jeong Min Lee · 1 shared paper, 2026 · strength 0.32
- Paper Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does i… DOI 10.1007/s00261-025-05271-6 ↗
Liang Zhu · 1 shared paper, 2026 · strength 0.32
- Paper Deep learning-based combined noise reduction and contrast enhancement for post-neoadjuvant pancreatic cancer CT: does i… DOI 10.1007/s00261-025-05271-6 ↗

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.