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Estimating Uncertainty of Geographic Atrophy Segmentations with Bayesian Deep Learning

Purpose: To apply methods for quantifying uncertainty of deep learning segmentation of geographic atrophy (GA). Design: Retrospective analysis of OCT images and model comparison. Participants: One hundred twenty-six eyes from 87 participants with GA in the SWAGGER cohort of the Nonexudative Age-Rela...

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Автори: Theodore Spaide, PhD, Anand E. Rajesh, MD, Nayoon Gim, Marian Blazes, MD, Cecilia S. Lee, MD, MS, Niranchana Macivannan, PhD, Gary Lee, PhD, MEng, Warren Lewis, MS, Ali Salehi, PhD, Luis de Sisternes, PhD, Gissel Herrera, MD, Mengxi Shen, MD, PhD, Giovanni Gregori, PhD, Philip J. Rosenfeld, MD, PhD, Varsha Pramil, MD, MS, Nadia Waheed, MD, MPH, Yue Wu, PhD, Qinqin Zhang, PhD, Aaron Y. Lee, MD, MSCI
Формат: Artigo
Мова:Inglês
Опубліковано: Elsevier 2025-01-01
Серія:Ophthalmology Science
Предмети:
Age-Related macular degeneration (AMD)
Bayesian deep learning
Geographic atrophy (GA)
Model uncertainty
OCT
Онлайн доступ:http://www.sciencedirect.com/science/article/pii/S2666914524001234
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