高效注意力金字塔网络在肺结节检测的应用

Efficient attention feature pyramid network for pulmonary nodule detection

针对肺部CT图像结节特征不明确和检测困难的问题,提出一种高效注意力特征金字塔网络。首先,该网络以多尺度特征融合的特征金字塔为主干网络,保留丰富的低层特征和高层特征,同时采用深度可分离卷积神经网络提取特征信息;然后,将注意力机制融入主干网络中,对重要特征信息进行加权处理;最后,将所提算法应用在Lung-PET-CT-Dx和Luna16数据集中。实验结果表明,本文算法的精度、召回率和mAP值均优于现有对比算法,证明本文算法在肺结节检测的优越性。

To address the challenge of unclear features and difficulties in pulmonary nodule CT image detection,an efficient attention feature pyramid network is proposed.The network firstly employs a feature pyramid of multi-scale feature fusion as the backbone network for effectively preserving both low-and high-level features,and uses the depthwise separable convolutional neural network to extract feature information.Then,the attention mechanism is integrated into the backbone network for assigning weights to salient feature information.Finally,the proposed algorithm is applied to Lung-PET-CT-Dx dataset and Luna16 dataset,and the experimental results demonstrate that the proposed algorithm has higher precision,recall rate and mAP value than the existing comparative algorithms,substantiating its superiority in pulmonary nodule detection.