arXiv (All)
open access
Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation
Automated radiology report generation from 3D CT volumes often suffers from incomplete pathology coverage. We provide empirical evidence that this limitation stems from a representational bottleneck: contrastive 3D CT embeddings encode discriminative pathology signals, yet exhibit severe dimensional
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
- Patient-Aware Contrastive Learning Preserves Per-Patient Structure in RR-Interval Representations#299940
- A Manifold-Aware Topic Modeling Approach via Rank-Based Prototypes#1077558
- Topological deep learning for drug-target interaction, virtual screening, and docking scoring: a practical, benchmark-driven review.#364674
- Frequency Schrödinger bridge diffusion model for fast MRI reconstruction in abdominal MR-guided online adaptive radiotherapy.#1299600
- Contrastive language image pretraining for a cardiac magnetic resonance image embedding with zero-shot capabilities.#220828
Record · ID 1216259
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