Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
Quick summary
arXiv:2604.16955v3 Announce Type: replace-cross Abstract: Stochastic generative models are increasingly used for longitudinal imaging, but their added complexity may provide limited benefit when predictable disease-related change is small relative to technical variability. We treat model-class selection (stochastic vs deterministic) as an empirical step determined by a task-adaptive diagnostic. For a longitudinal image prediction task complicated by irregular follow-up, acquisition variability, and device heterogeneity, the diagnostic asks whether inter-visit image change is driven by time-dep
Key takeaways
- arXiv:2604.16955v3 Announce Type: replace-cross Abstract: Stochastic generative models are increasingly used for longitudinal imaging, but their added complexity may provide limited benefit when predictable disease-related change is small relative to technical variability.
- We treat model-class selection (stochastic vs deterministic) as an empirical step determined by a task-adaptive diagnostic.
- For a longitudinal image prediction task complicated by irregular follow-up, acquisition variability, and device heterogeneity, the diagnostic asks whether inter-visit image change is driven by time-dep
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