Diagnostic-Guided Longitudinal Modeling for Forecasting Retinal Atrophy Progression
cs.CV, cs.AI, cs.LG
Submitted: 2026-04-18
Updated: 2026-09-14
Code: https://github.com/Liyincly01/TRU_longitudinal_retinal_image_prediction
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
Terminology
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-dependent disease progression signals or time-independent acquisition variability. If a stochastic approach fails to yield useful predictive diversity, a more parsimonious, deterministic model class is selected. We applied this strategy to fundus autofluorescence (FAF) future image prediction. Applied to a heterogeneous Optos FAF archive (24,335 images from 9,708 eyes), the diagnostic indicated that global inter-visit FAF change was weakly associated with elapsed time, and source-anchored stochastic configurations produced little endpoint-resolvable sample variation. Guided by the diagnostic, we developed Temporal Retinal U-Net (TRU), a deterministic single-pass predictor conditioned on irregular imaging history and prediction horizon. Evaluated on a held-out cohort and two independent zero-shot transfer cohorts spanning rare-disease and cross-vendor shift, TRU achieved the strongest overall performance among the evaluated classical and deep-learning comparators on image-level and eye-specific progression measures, with lower precision in the smaller cross-vendor cohort. These findings support a diagnostic-guided strategy for longitudinal image prediction and demonstrate an example where deterministic predictions outperform stochastic models.
Sources
- Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging
- Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation
- Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration
- Progressive Distillation for Fast Sampling of Diffusion Models
- SGDR: Stochastic Gradient Descent with Warm Restarts
- Auto-Encoding Variational Bayes
- Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching
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