arXiv (All)
open access
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 de
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Longitudinal Risk Prediction in Mammography with Privileged History Distillation#997474
- Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Data#1143703
- LLM-based reconstruction of longitudinal clinical trajectories in chronic liver disease#387618
- Systemic sclerosis: Characterizing candidate retinal and choroidal imaging features using optical coherence tomography.#122867
- Machine Learning Model Predicts Monoclonal Gammopathy Using Routine Laboratory Values.#227784
Record · ID 1034724
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive