Xijun Lu · Hongying Liu · Fanhua Shang · Yanming Hui · Liang Wan
Tianjin University · Medical School & College of Intelligence and Computing

PDD🛍️🛒is a novel framework for medical image anomaly detection, designed to tackle subtle, heterogeneous anomalies in complex anatomical structures. Addressing the limitations of traditional Grad-CAM methods on medical data, PDD innovatively unifies dual-teacher priors— frozen VMamba-Tiny (global context) and ResNet50 (local structure)—into a shared high-dimensional manifold.

• [2026/02/21] PDD is accepted to CVPR 2026 🔥. The code will be released before June.

• Release the code of PDD

If you find our code or paper useful, please cite

@misc{lu2026pddmanifoldpriordiversedistillation,
      title={PDD: Manifold-Prior Diverse Distillation for Medical Anomaly Detection}, 
      author={Xijun Lu and Hongying Liu and Fanhua Shang and Yanming Hui and Liang Wan},
      year={2026},
      eprint={2603.07142},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.07142}, 
}