You can efficiently adapt pre-trained medical imaging models to understand anatomy-level details by decomposing scans into regions, learning how they relate to each other, and aligning them with medical text—without retraining from scratch.
This paper presents Anatomy Contextualized Adaptation (ACA), a lightweight method that improves CT scan foundation models by aligning fine-grained anatomical regions with text descriptions from radiology reports while preserving whole-scan context.