arXiv:2610.30584v1 Announce Type: new Abstract: Chest X-ray interpretation depends on both the anatomical location of findings and their relationships with other abnormalities. However, relations defined over global finding labels do not explicitly distinguish the same finding across anatomical locations. We propose CoRE(Co-occurrence-guided Region Evidence), a framework that incorporates relationships among findings and anatomical context into fine-grained chest X-ray prediction. It groups anatomical region-finding labels by co-occurrence and uses these groups to guide shared representation learning through visual prompt tuning on a frozen vision transformer. The predictions are supplemented by an anatomically supervised region-evidence stream through region-matched logit residuals. Experiments on MIMIC-CXR with Chest ImaGenome annotations show that CoRE improves region-finding classification over standard and relation-aware prompt-based baselines. Quantitative evaluations on MS-CXR and the Chest ImaGenome gold standard show improved finding localization, supported by qualitative comparisons. CoRE also improves finding-level classification on mapped finding labels in external VinDr-CXR and CheXpert datasets without target-domain adaptation. These results demonstrate the value of jointly modeling finding relationships and anatomical context for fine-grained chest X-ray analysis.