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 concentration, with as few as 2 effective dimensions out of 512. Corroborating this, scaling the language model yields no measurable improvement, suggesting that the bottleneck lies in the visual representation rather than the generator. This bottleneck limits both generation and retrieval; naive static retrieval fails to improve clinical efficacy and can even degrade performance. We propose \...
| Authors | Renjie Liang, Yiling Ma, Yang Xing et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 March 2026 |
| Last updated at source | 5 October 2026 |
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