Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification
This model offers a promising solution for advancing OCT image classification, achieving high accuracy while reducing the cost of extensive expert annotation and potentially streamlining clinical workflows, thereby supporting more efficient patient management.
Authors
- Sungho Shim (ORCID: https://orcid.org/0000-0002-2235-6142)
- Jae Rock (ORCID: https://orcid.org/0000-0002-0129-0682)
- Yong Koo Kang (ORCID: https://orcid.org/0000-0002-6505-315X)
- Hyun-Lim Yang (ORCID: https://orcid.org/0000-0003-2221-3042)
- Min Soo Kim (ORCID: https://orcid.org/0000-0002-8901-7241)
- Hong Kyun Kim (ORCID: https://orcid.org/0000-0002-9283-3506)
- Che Gyem Yae
Institutions
- Seoul National University (KR)
- Korea Advanced Institute of Science and Technology (KR)
- Daegu Gyeongbuk Institute of Science and Technology (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- Kyungpook National University (KR)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1093/jamia/ocaf021
- Citations
- 2
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Research Foundation of Korea
- Kyungpook National University Hospital
- Ministry of Science and ICT, South Korea