Localization of Polypoidal Choroidal Vasculopathy in Fluorescein Angiography Using Semisupervised Deep Learning With Labeled and Unlabeled Images
The developed deep learning system eliminates the need for indocyanine green angiography in training an effective model for monitoring polypoidal choroidal vasculopathy treatment.
Authors
- Chia-Ling Tsai (ORCID: https://orcid.org/0000-0002-6234-025X)
- Shih‐Jen Chen (ORCID: https://orcid.org/0000-0001-9798-1578)
- Yeou‐Ping Tsao (ORCID: https://orcid.org/0009-0005-0821-3726)
- Paisan Ruamviboonsuk
- Chih‐Yang Lin (ORCID: https://orcid.org/0000-0002-0401-8473)
- Wei-Yang Lin (ORCID: https://orcid.org/0000-0003-0895-2498)
- Tsung‐Tien Wu (ORCID: https://orcid.org/0000-0002-9020-8248)
Institutions
- Queens College, CUNY (US)
- National Chung Cheng University (TW)
- National Yang Ming Chiao Tung University (TW)
- Taipei Veterans General Hospital (TW)
- Rajavithi Hospital (TH)
- Kaohsiung Veterans General Hospital (TW)
- Rangsit University (TH)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2025-10-14
- DOI
- https://doi.org/10.1167/tvst.14.10.15
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- City University of New York
- National Science and Technology Council