Boosting Anatomical Retrieval Accuracy with Re-Ranking Methods

This article explores how re-ranking methods enhance retrieval recall across anatomical structures in AI models. By applying re-ranking, all evaluated models—DreamSim, DINOv1, and SwinTransformer—show improved performance. While DreamSim consistently achieves the best results in region-based and localized retrieval, DINOv1 and SwinTransformer also excel in specific conditions. The findings highlight how re-ranking not only raises recall rates but also strengthens localization, proving its critical role in medical imaging and anatomical AI systems.