Using preference-based learning (DPO) with structural constraints rather than pixel-level metrics can fix a fundamental problem in medical image segmentation: producing fragmented, unrealistic vessel structures despite high accuracy scores.
ARIADNE combines vision-language models with reinforcement learning to detect coronary artery blockages in medical images while maintaining the correct structure of blood vessels. Instead of just matching pixels, it uses topological constraints to ensure vessel networks stay connected, reducing false alarms by 41% and achieving better accuracy on real clinical data.