细粒度放射学报告检索的时空与临床条件化
Spatio-Temporal and Clinical Conditioning for Fine-Grained Radiology Report Retrieval
AI总结：
提出STAR3框架，通过区域级解剖信息与临床指征及纵向变化对齐，实现基于检索的放射学报告生成，在MIMIC-CXR上优于现有方法。
摘要：
放射学对现代医疗至关重要，但不断增长的成像需求和持续的人力短缺给报告生成能力和临床工作流程带来压力。自动化放射学报告生成有潜力支持放射科医生并帮助减轻这一负担；然而，现有的基于检索的方法仍然僵化，缺乏明确的解剖基础，并且没有考虑纵向疾病进展或可用的临床背景。在这项工作中，我们引入了STAR3，一种用于放射学报告生成的多模态、时空、注意力检索框架，该框架将区域级解剖信息与临床指征以及胸部X光研究中的纵向变化对齐。我们的框架采用目标检测器来识别解剖上有意义的区域，并基于当前临床背景以及先前和当前检查之间观察到的变化检索语义相关的报告句子。这种设计使得能够生成基于解剖和时间的报告，更好地反映临床报告实践。在MIMIC-CXR数据集上的实验表明，STAR3在检索、NLP和临床指标上优于当前的基于检索的方法，突显了在解剖、时间和临床方面条件化检索对于推进自动化放射学报告生成的价值。
英文摘要：
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. Our framework employs an object detector to identify anatomically meaningful regions and retrieves semantically relevant report sentences conditioned on both current clinical context and changes observed between prior and current examinations. This design enables anatomically and temporally grounded report generation that better reflects clinical reporting practice. Experiments on the MIMIC-CXR dataset demonstrate that STAR3 outperforms current retrieval-based approaches on retrieval, NLP and clinical metrics, highlighting the value of conditioning retrieval anatomically, temporally and clinically for advancing automated radiology report generation.
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