Early screening based on computed tomography (CT) pulmonary nodule detection is an important means to reduce lung cancer mortality, and in recent years three dimensional convolutional neural network (3D CNN) has achieved success and continuous development in the field of lung nodule detection. We proposed a pulmonary nodule detection algorithm by using 3D CNN based on a multi-scale attention mechanism. Aiming at the characteristics of different sizes and shapes of lung nodules, we designed a multi-scale feature extraction module to extract the corresponding features of different scales. Through the attention module, the correlation information between the features was mined from both spatial and channel perspectives to strengthen the features. The extracted features entered into a pyramid-similar fusion mechanism, so that the features would contain both deep semantic information and shallow location information, which is more conducive to target positioning and bounding box regression. On representative LUNA16 datasets, compared with other advanced methods, this method significantly improved the detection sensitivity, which can provide theoretical reference for clinical medicine.
基于电子计算机断层扫描（CT）的肺结节检测的早期筛查是降低肺癌死亡率的重要手段，而近年来三维卷积神经网络（3D CNN）已经在肺结节检测领域取得了成功并不断深入发展。本文提出了一种基于多尺度注意力机制的3D CNN肺结节检测算法。针对肺结节大小和形状各异的特点，设计了一个多尺度的特征提取模块，提取不同尺度的相应特征。通过注意力模块，从空间和通道两个角度挖掘特征间的关联信息，对特征加强。提取出的特征进入类似金字塔的融合机制，使得特征中同时包含深层的语义信息与浅层的位置信息，更利于目标定位与边界框回归。在具有代表性的LUNA16数据集上，相对于目前先进的其他方法，本文方法能够明显地提高检测灵敏度，可为临床医学提供理论参考。.
Keywords: Attention mechanism; Multi-scale feature extraction; Pulmonary nodule detection; Three dimensional convolutional neural network.