一种结合三重注意力机制的双路径网络胸片疾病分类方法

近年来，利用CNN进行医学图像处理，在胸片疾病分类任务中取得显著研究进展。然而，与单一结构CNN相比，双路径网络可结合不同CNN特点，从而提高疾病分类能力。其次，对于不同疾病，其位置、大小、形态、密度、纹理等特征均有不同，而注意力机制有助于模型提取不同病理特征，提升分类精度。因此针对胸片疾病分类问题，该文提出一种结合三重注意力机制的双路径卷积神经网络(TADPN)，TADPN将ResNet和DenseNet结合的双路径网络DPN作为骨干网络，并利用3种不同形式的注意力机制改进DPN，在维持参数量稳定的同时提高网络复杂度，进而提升对胸片疾病的分类精度。在ChestXray14数据集上实验，并与目前较为先进的6种算法对比，14种疾病的平均AUC值达到0.8185，较前人提升1.1%，表明双路径CNN及三重注意力机制对胸片疾病分类的有效性及TADPN的先进性。

A Dual-path Network Chest Film Disease Classification Method Combined with a Triple Attention Mechanism

In recent years, medical image processing with CNN has made remarkable research progress in the task of chest film disease classification. However, compared with single structure CNN, dual-path network can combine the characteristics of different CNN to improve the ability of disease classification. Secondly, for different diseases, their location, size, shape, and texture are different, the attention mechanism helps the model to extract different pathological features and improve the classification accuracy. Therefore, focusing on the chest film disease classification problem, a dual path convolution neural network TADPN(Triple Attention Dual Path Network) combined with a triple attention mechanism is proposed. TADPN takes the dual-path network combined with ResNet and DenseNet as the backbone network and uses three different forms of attention mechanisms to improve the backbone network. The network complexity and classification accuracy are improved while maintaining the stability of the parameters. In this paper, the validity of TADPN is compared with the six advanced algorithms on the ChestXray14 dataset. The experiments show the progressiveness of the dual-path CNN and the triple attention mechanism, as well as the effectiveness of TADPN. The average AUC value of 14 diseases reaches 0.8185, which is 1.1% higher than that of previous generations.