arXiv:2606. 30417v1 Announce Type: cross Abstract: Forecasting visual fields (VFs) is critical for personalized monitoring and treatment planning in glaucoma.
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Deep learning framework GLAM predicts glaucoma progression rates from longitudinal Humphrey 24‑2 visual field data and five clinical features, achieving a mean absolute error of 0.139 dB yr⁻¹ and an AUC of 0.990 for fast‑progressor detection. Using attention‑based fusion and aleatoric uncertainty, GLAM outperforms a ridge regression baseline by 73.5% in MD‑rate prediction. The model demonstrates that visual field data alone can match multimodal pipelines for progression prognostication.
The study proposes a diagnostic-guided approach to choose between stochastic and deterministic longitudinal imaging models based on whether inter-visit changes are driven by disease progression or acquisition variability. Applied to a large, heterogeneous Optos fundus autofluorescence archive, the diagnostic revealed weak time-dependent changes and limited benefit from stochastic models, leading to the development of the deterministic Temporal Retinal U‑Net (TRU). TRU outperformed other classical and deep‑learning comparators on image‑level and eye‑specific progression metrics across held‑out and independent zero‑shot transfer cohorts, though with slightly lower precision in a smaller cross‑vendor cohort.
The paper introduces an ordinal latent diffusion model for generating color fundus images that incorporates the ordered structure of diabetic retinopathy (DR) severity, using a scalar disease representation instead of categorical conditioning. Evaluations on the EyePACS dataset show improved visual realism, with reduced Fréchet inception distance for most stages and a higher quadratic weighted κ from 0.79 to 0.87. Interpolation experiments demonstrate the model captures a continuous spectrum of disease progression derived from coarse, ordered labels.
arXiv:2508. 03750v2 Announce Type: replace Abstract: Early and accurate glaucoma detection is critical to prevent irreversible vision loss, yet existing AI methods often rely on unimodal inputs and lack interpretability.
arXiv:2512. 08029v3 Announce Type: replace Abstract: Clinical decision-making in oncology requires predicting dynamic disease evolution, a task current static AI predictors cannot perform.
The paper investigates how pre‑training strategy, dataset size, and domain affect uncertainty estimation in vision medical foundation models. It compares point‑prediction calibration with conformal (region) prediction across retinal, histopathological, and chest X‑ray models, finding that domain‑specific, self‑supervised pre‑training yields better calibration and more efficient conformal sets. The study shows that standard recalibration alone cannot fully reconcile uncertainty differences between models trained on different data sources.