自動化結構化放射學報告生成與豐富臨床背景

摘要

從胸部X光影像自動生成結構化放射學報告（SRRG）具有顯著潛力，能通過生成符合臨床報告標準的結構化報告來減輕放射科醫師的工作負擔。儘管放射科醫師在診斷推理中有效利用了可用的臨床背景，現有的SRRG系統卻忽略了這些關鍵要素。這一根本性差距導致了包括在引用不存在的臨床背景時出現的時間性幻覺等嚴重問題。為解決這些限制，我們提出了情境化SRRG（C-SRRG），全面整合豐富的臨床背景以支持SRRG。我們通過整合涵蓋1）多視角X光影像、2）臨床指徵、3）成像技術以及4）基於患者病史的先前研究及其對比，來策劃C-SRRG數據集。通過與最先進的多模態大型語言模型進行廣泛的基準測試，我們證明結合臨床背景的C-SRRG顯著提升了報告生成的質量。我們公開了數據集、代碼和檢查點，以促進未來在臨床對齊的自動化RRG研究，詳見https://github.com/vuno/contextualized-srrg。

English

Automated structured radiology report generation (SRRG) from chest X-ray images offers significant potential to reduce workload of radiologists by generating reports in structured formats that ensure clarity, consistency, and adherence to clinical reporting standards. While radiologists effectively utilize available clinical contexts in their diagnostic reasoning, existing SRRG systems overlook these essential elements. This fundamental gap leads to critical problems including temporal hallucinations when referencing non-existent clinical contexts. To address these limitations, we propose contextualized SRRG (C-SRRG) that comprehensively incorporates rich clinical context for SRRG. We curate C-SRRG dataset by integrating comprehensive clinical context encompassing 1) multi-view X-ray images, 2) clinical indication, 3) imaging techniques, and 4) prior studies with corresponding comparisons based on patient histories. Through extensive benchmarking with state-of-the-art multimodal large language models, we demonstrate that incorporating clinical context with the proposed C-SRRG significantly improves report generation quality. We publicly release dataset, code, and checkpoints to facilitate future research for clinically-aligned automated RRG at https://github.com/vuno/contextualized-srrg.

自動化結構化放射學報告生成與豐富臨床背景

Automated Structured Radiology Report Generation with Rich Clinical Context

摘要

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