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• PDD (Manifold-Prior Diverse Distillation) significantly outperforms existing state-of-the-art methods in medical image anomaly detection.result 95%
• PDD achieves improvements of up to 11.8%, 5.1%, and 8.5% in AUROC on HeadCT, BrainMRI, and ZhangLab datasets respectively.result 95%
• PDD improves F1 max by 3.4% on the Uni-Medical dataset.result 95%
• Discriminative activation maps (like Grad-CAM) fail on medical data unlike their success on industrial datasets.technical 85%
• PDD uses frozen VMamba-Tiny and wide-ResNet50 encoders to provide global contextual and local structural priors respectively.method 90%
• PDD employs a diversity loss to prevent representation collapse while maintaining detection sensitivity.method 90%
• Medical image anomaly detection faces challenges due to subtle, heterogeneous anomalies embedded in complex anatomical structures.technical 85%
• The implementation of PDD will be publicly released on GitHub.method 95%

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