arXiv (All)
open access
Confidence-Gated Cloud-Edge Cascade Triage via Variational Risk Minimization for Medical Imaging
Emergency chest X-ray (CXR) triage has a structural modality gap: reports arrive after triage decisions, yet multimodal foundation models require image-text inputs. We present Variational Risk Minimization (VRM), a distillation framework that treats LVLM-generated report variants as Monte Carlo samp
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 Specialized Large Multimodal Model for Interpreting PET/CT in Head and Neck Cancer#972593
- Beyond Captions: Context-Grounded Reconstruction for Biomedical Multimodal Continued Pretraining#1232756
- Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations#1082919
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.#1359075
- From Classical Machine Learning to Emerging Foundation Models: Review on Multimodal Data Integration for Cancer Research#163169
Record · ID 1186583
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