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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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Principais autores: 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
Formato: Artigo
Idioma:Inglês
Publicado em: Elsevier 2025-01-01
coleção:Ophthalmology Science
Assuntos:
Age-Related macular degeneration (AMD)
Bayesian deep learning
Geographic atrophy (GA)
Model uncertainty
OCT
Acesso em linha:http://www.sciencedirect.com/science/article/pii/S2666914524001234
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