Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism

作者：Wanling Liu, Wenhuan Lu, Yijian Li, Fei Chen, Fan Jiang, Jianguo Wei, Bo Wang, Wenxin Zhao · 发表于：Electronics · 年份：2025 · DOI：10.3390/electronics14061092 · 被引用次数：4 · 研究领域：Advanced Neural Network Applications、Medical Image Segmentation Techniques、Face and Expression Recognition

While deep learning techniques, such as Convolutional neural networks (CNNs), show significant potential in medical applications, real-time detection of parathyroid glands (PGs) during complex surgeries remains insufficiently explored, posing challenges for surgical accuracy and outcomes. Previous studies highlight the importance of leveraging prior knowledge, such as shape, for feature extraction in detection tasks. However, they fail to address the critical multi-scale variability of PG objects, resulting in suboptimal performance and efficiency. In this paper, we propose an end-to-end framework, MSWF-PGD, for Multi-Scale Weighted Fusion Parathyroid Gland Detection. To improve accuracy and efficiency, our approach extracts feature maps from convolutional layers at multiple scales and re-weights them using cluster-aware multi-scale alignment, considering diverse attributes such as the size, color, and position of PGs. Additionally, we introduce Multi-Scale Aggregation to enhance scale interactions and enable adaptive multi-scale feature fusion, providing precise and informative locality information for detection. Extensive comparative experiments and ablation studies on the parathyroid dataset (PGsdata) demonstrate the proposed framework’s superiority in accuracy and real-time efficiency, outperforming state-of-the-art models such as RetinaNet, FCOS, and YOLOv8.