Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors

arXiv:2609.29256 · cs.CV, cs.AI · Submitted 2026-09-24 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-24

Updated: 2026-09-24

Code: https://github.com/taiabrbd/glam

Terminology

• AUC — 13× in this paper · explained in 2 episodes
• Aleatoric Uncertainty — 8× in this paper · explained in 4 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Empirical Coverage — 3× in this paper · explained in 2 episodes
• University of Washington Humphrey Visual Field — 3× in this paper
• Analyzer — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Glaucoma Longitudinal Analysis Model — 2× in this paper
• Humphrey Field Analyzer — 2× in this paper
• Ablation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Decision-Support Tool — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• F1 Score — 1× in this paper · explained in 3 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hybrid Architectures — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• LayerNorm and Gaussian Error Linear Unit — 1× in this paper
• Longitudinal Data — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes

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