Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
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-dependent disease progression signals or time-independent acquisition variability. If a stochastic approach fails to yield useful predictive diversity, a more...
| Authors | Liyin Chen, Souvick Mukherjee, Ines Maria De Carvalho Lains et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 April 2026 |
| Last updated at source | 14 September 2026 |
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