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<p>In prenatal ultrasound, accurately identifying fetal abdominal ultrasound standard planes (FAUSP) is challenging due to the complexity of anatomical structures. To address this, we developed FAUSP-NET, a multi-task network that integrates a mixed attention mechanism for real-time FAUSP recognition and anatomical structure detection. The network uses a residual backbone for feature extraction, enhanced by an attention mechanism and Large Selective Kernel Block (LSKblock) for better focus on key regions. A Focal_EIoU loss function addresses class imbalance and improves bounding box regression. Trained on 6767 FAUSP images, FAUSP-NET outperforms 24 popular models, achieving mAP@0.5 of 0.961 and mAP@0.5:0.95 of 0.653 in detection, with plane recognition accuracy of 0.972. Its average detection time is 24.1 ms. Doctor evaluations show that FAUSP-NET’s accuracy is comparable to senior physicians, offering significant support for clinical ultrasound diagnostics.</p>

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Mixed attention mechanism multi-task learning for fetal abdominal standard plane recognition and key anatomical structure detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月14日

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  • Yapeng Li,
  • Yubing Huang,
  • Zhonghua Liu,
  • Tianen Fu,
  • Shaozheng He,
  • Yaocheng Wan,
  • Yuling Fan,
  • Peizhong Liu,
  • Guorong Lyu,
  • Shunlan Liu

摘要

In prenatal ultrasound, accurately identifying fetal abdominal ultrasound standard planes (FAUSP) is challenging due to the complexity of anatomical structures. To address this, we developed FAUSP-NET, a multi-task network that integrates a mixed attention mechanism for real-time FAUSP recognition and anatomical structure detection. The network uses a residual backbone for feature extraction, enhanced by an attention mechanism and Large Selective Kernel Block (LSKblock) for better focus on key regions. A Focal_EIoU loss function addresses class imbalance and improves bounding box regression. Trained on 6767 FAUSP images, FAUSP-NET outperforms 24 popular models, achieving mAP@0.5 of 0.961 and mAP@0.5:0.95 of 0.653 in detection, with plane recognition accuracy of 0.972. Its average detection time is 24.1 ms. Doctor evaluations show that FAUSP-NET’s accuracy is comparable to senior physicians, offering significant support for clinical ultrasound diagnostics.