AI-powered hierarchical classification of ampullary neoplasms: a deep learning approach using white-light and narrow-band imaging
A hierarchical deep learning approach integrating dual-modality imaging and synthetic data augmentation significantly improves diagnostic performance for ampullary lesions.
Authors
- S. Lee
- Young Gyun Kim (ORCID: https://orcid.org/0000-0003-1231-9097)
- Sung Hoon Chang (ORCID: https://orcid.org/0000-0001-5516-1125)
- Hyunsoo Chung (ORCID: https://orcid.org/0009-0007-8132-1293)
- Joo Seong Kim (ORCID: https://orcid.org/0000-0002-0411-9805)
- Dan Yoon (ORCID: https://orcid.org/0000-0002-5657-5984)
- Seong Ji Choi (ORCID: https://orcid.org/0000-0002-1969-516X)
- Jin Ho Choi (ORCID: https://orcid.org/0000-0003-2758-1370)
- In Rae Cho
- Kim Sm
- Ji Kon Ryu
- Byeong Soo Kim
- Woo Hyun Paik
Institutions
- Seoul National University (KR)
- Korea University (KR)
- American Society for Gastrointestinal Endoscopy (US)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- Dongguk University Ilsan Hospital (KR)
- National University College (PR)
- MTA-SZTE Research Group on Artificial Intelligence (HU)
Publication Details
- Journal
- Surgical Endoscopy
- Published
- 2026-01-14
- DOI
- https://doi.org/10.1007/s00464-025-12534-2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Seoul National University
- Seoul National University Hospital