arXiv (OAI Expanded)
open access
Self-supervised Pre-training Helps Retinal Disease Progression Modelling Most When Data Is Scarce
Modelling how a disease progresses over time requires longitudinal imaging cohorts, which are scarce and small, whereas cross-sectional data -- one image per participant -- is abundant. Self-supervised pre-training on such data offers a way to bridge this gap, but it is unclear which strategy best s
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
- A Controlled Study of Self-Supervised Image and Video Pretraining under Limited Resources#1105141
- Prediction of Progression of Retinal Ischemia in Diabetes#827044
- Established Machine Learning Matches Tabular Foundation Models in Clinical Predictions#422479
- Prediction of structural glaucoma progression from baseline fundus photographs using deep learning: a retrospective multicentre study.#446703
- Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors#1077185
Record · ID 1070294
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