Robust Uncertainty-Informed Glaucoma Classification Under Data Shift
This study introduces a tunable framework that explores the trade-off between prediction accuracy and data retention in glaucoma prediction. By managing uncertainty in model outputs, the approach lays a foundation for future decision support tools aimed at improving the reliability of automated glaucoma diagnosis.
Authors
- Yannek I. Leiderman
- Thasarat S. Vajaranant (ORCID: https://orcid.org/0000-0002-3737-2559)
- Darvin Yi
- R.V. Paul Chan (ORCID: https://orcid.org/0000-0002-1971-2249)
- Homa Rashidisabet (ORCID: https://orcid.org/0000-0003-4819-6113)
Institutions
- Duke University (US)
- University of Illinois Chicago (US)
Publication Details
- Journal
- Translational Vision Science & Technology
- Published
- 2025-06-02
- DOI
- https://doi.org/10.1167/tvst.14.6.3
- Citations
- 1
- Primary Topic
- Retinal Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Research to Prevent Blindness
- Regeneron Pharmaceuticals
- Genentech