A Modular Agent for Reliable and Auditable Spatial Relation Verification in CT Scans
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...
| Authors | Simon Vincent Abel, Heiko Hillenhagen, Michael Götz et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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