用於CT掃描中可靠且可稽核之空間關係驗證的模組化代理

摘要

可靠的空間理解是未來醫學視覺語言系統的重要先決條件，這些系統旨在支援放射學報告生成與結構化影像理解。儘管現代視覺語言模型（VLM）在許多醫學影像任務上展現出優異表現，近期的證據顯示它們在受控空間推理方面仍然薄弱，且常無法可靠地將空間關聯錨定於影像證據之中。鑑於放射學推理高度依賴於理解解剖結構與病灶之間的相對位置，此空間理解能力的不足對診斷準確性構成風險。我們提出了一種模組化醫學影像代理，用於軸向CT切片中的二元空間關係驗證。該系統並非直接以端到端方式預測空間答案，而是將任務分解為明確的階段：語言解析、解剖定位與確定性幾何驗證。自然語言查詢會被轉換為結構化關係元組，查詢的器官經由基於YOLO的檢測器進行定位，最終的空間決策則利用確定性幾何規則由物體中心計算得出。我們在保留的MIRP空間問答基準上評估此方法，並將其與具代表性的端到端VLM基線進行比較。效能最佳的混合配置達到了94.1%的準確率與94.2%的F1分數，在準確率上較直接使用Qwen2-VL提示高出42.5個百分點，同時保留了可解釋的中間表示與可稽核的推理階段。結果表明，明確的模組化空間驗證可作為未來以報告為導向之醫學影像代理富有前景的建構基礎。

English

Reliable spatial understanding is an important prerequisite for future medical vision-language systems that aim to support radiological report generation and structured image understanding. While modern vision-language models (VLMs) show promising performance on many medical imaging tasks, recent evidence suggests they remain weak in controlled spatial reasoning and often fail to reliably ground spatial relations in image evidence. Given that radiological reasoning hinges on understanding the relative positions of anatomical structures and findings, this spatial weakness poses risks to diagnostic accuracy. We present a modular medical imaging agent for binary spatial relation verification in axial CT slices. Instead of directly predicting spatial answers end-to-end, the system decomposes the task into explicit stages: language parsing, anatomical localization, and deterministic geometric verification. Natural-language queries are converted into structured relation tuples, queried organs are localized with a YOLO-based detector, and the final spatial decision is computed from object centers using deterministic geometric rules. We evaluate the approach on the held-out MIRP spatial QA benchmark and compare it against representative end-to-end VLM baselines. The best-performing hybrid configuration reaches 94.1% accuracy and 94.2% F1, outperforming direct Qwen2-VL prompting by 42.5 percentage points in accuracy, while preserving interpretable intermediate representations and auditable reasoning stages. The results suggest that explicit modular spatial verification can serve as a promising building block for future report-oriented medical imaging agents.