Spatio-Temporal and Clinical Conditioning for Fine-Grained Radiology Report Retrieval
Radiology is vital to modern healthcare, but rising imaging demand and persistent workforce shortages strain reporting capacity and clinical workflows. Automated radiology report generation has the potential to support radiologists and help alleviate this burden; however, existing retrieval-based methods remain rigid, lack explicit anatomical grounding, and do not account for longitudinal disease progression or available clinical context. In this work, we introduce STAR3, a multimodal, spatio-temporal, attentive retrieval framework for radiology report generation that aligns region-level anatomical information with clinical indications and longitudinal changes across chest X-ray studies. ...
| Authors | P. Sloan, E. Simpson, M. Mirmehdi |
|---|---|
| Primary category | cs.CV |
| Published | 2 July 2026 |
| Last updated at source | 2 July 2026 |
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