Beyond Static Anchors: Bounded Prototype Conditioning for Language-Free Medical Anomaly Detection
The episode discusses a paper proposing ReCAP, a method for language-free medical anomaly detection that replaces static anchors with input-conditioned visual prototypes. The system adapts prototypes per image using bounded modulation to prevent them from shifting toward anomalies. It achieves high accuracy across six datasets and reduces inference time by over seventy percent.