arXiv (All)
open access
Pseudo-Label Foreground Quality Weighting for Domain-Generalized Fundus Image Segmentation
Optic disc (OD) and optic cup (OC) segmentation is central to automated glaucoma assessment, but models trained on limited labeled fundus datasets often degrade under domain shift. Semi-supervised Teacher-Student learning can improve generalization by leveraging unlabeled data, yet standard Noisy St
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
- Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training#820192
- Point-Supervised Visual Learning for GNSS Measurement Quality Assessment#1325718
- A Study to Evaluate the Clinical Performance of VeriSee GLC for Screening of Glaucoma Suspect Using Color Fundus Images#290895
- Partial Least Squares Regression Modelling for Glaucoma Detection#125986
- Clinical Significance of Optic Disc Hemorrhage Size in Visual Field Progression in Glaucoma#1257164
Record · ID 1388478
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