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MedAD-R1: Consistency-Reinforced Policy Optimization for Interpretable Medical Anomaly Detection
Medical Anomaly Detection (MedAD) offers a promising direction for medical image analysis with Large Multimodal Models (LMMs). However, progress is limited by fragmented datasets and the tendency of Supervised Fine-Tuning (SFT) to learn superficial image-text correlations rather than verifiable diag
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