PEYOLO: a wrist fracture detection network based on multi-level receptive field feature extraction and cross-scale fusion - Takeaways - MDSpire

PEYOLO: a wrist fracture detection network based on multi-level receptive field feature extraction and cross-scale fusion

  • By

  • Shuwei Zhang

  • Rong Tang

  • Jiong Mu

  • Shaohai Ren

  • May 11, 2026

  • 0 min

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  • 1

    PEYOLO is a novel model designed for wrist fracture detection using multi-level feature extraction and cross-scale fusion techniques.

  • 2

    The Parallel Dilated Multi-head Attention Module (PDMAM) enables the model to capture multi-scale features effectively.

  • 3

    An Efficient Multi-Scale Attention module enhances the perception of fracture features by integrating multi-scale spatial information.

  • 4

    Experimental results show that PEYOLO improves mean Average Precision (mAP) by 1.4% over the baseline and outperforms existing models.

  • 5

    PEYOLO demonstrates high precision and fast inference speed, making it a valuable tool for clinical diagnosis of wrist fractures.

Original Source(s)

PEYOLO: a wrist fracture detection network based on multi-level receptive field feature extraction and cross-scale fusion

  • by Shuwei Zhang, Rong Tang, Jiong Mu, Shaohai Ren

  • May 11, 2026

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