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This paper introduces a modular agent designed for reliable binary spatial relation verification in axial CT scans, addressing the limitations of existing vision-language models in spatial reasoning. By decomposing the verification task into distinct stages鈥攍anguage parsing, anatomical localization, and geometric verification鈥攖he system achieves a significant accuracy improvement over end-to-end models. The proposed hybrid configuration reaches 94.1% accuracy, outperforming the Qwen2-VL baseline by 42.5 percentage points, while maintaining interpretable and auditable reasoning processes.
Achieving 94.1% accuracy in spatial relation verification, this modular agent outperforms traditional models by a staggering 42.5 percentage points, highlighting the power of structured reasoning in medical imaging.
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.
