论文标题
FPAENET：基于特征金字塔注意增强的肺炎检测网络
FPAENet: Pneumonia Detection Network Based on Feature Pyramid Attention Enhancement
论文作者
论文摘要
基于深度学习的自动肺炎检测具有增加的临床价值。尽管现有的特征金字塔网络（FPN）及其变体已经取得了巨大的成功，但它们在医学图像中对肺炎病变的检测准确性仍然不令人满意。在本文中，我们根据特征金字塔注意的增强提出了一个肺炎检测网络，该网络将参加的高级语义特征与低级信息集成在一起。我们添加了另一个具有功能增强模块的信息提取路径，这些路径是通过注意机制进行的。实验结果表明，与检测肺炎病变的基准相比，我们提出的方法可以取得更好的性能，高4.02％和3.19％。
Automatic pneumonia Detection based on deep learning has increasing clinical value. Although the existing Feature Pyramid Network (FPN) and its variants have already achieved some great successes, their detection accuracies for pneumonia lesions in medical images are still unsatisfactory. In this paper, we propose a pneumonia detection network based on feature pyramid attention enhancement, which integrates attended high-level semantic features with low-level information. We add another information extracting path equipped with feature enhancement modules, which are conducted with an attention mechanism. Experimental results show that our proposed method can achieve much better performances, as a higher value of 4.02% and 3.19%, than the baselines in detecting pneumonia lesions.
