# Adaptive Aggregated Attention Network for Pulmonary
## 2 Related Works  
Traditionally, researchers extracted hand-crafted features based on their professional recognition in medical imaging and pulmonary nodules and then designed a classifier to classify the extracted hand-crafted features. The hand-crafted features usually included texture, shape, density, and sphericity features, and the classifiers usually included SupportVector Machine (SVM), Random Forest, and K-nearest classifier [8; 9; 10]. [PERSON] et al. [11] utilized image preprocessing and pattern recognition methods to differentiate malignant and benign nodules. To classify nodules, they used phylogenetic diversity by means of particular indexes, which were intensive quadratic entropy, extensive quadratic entropy, average taxonomic distinctness, and so on. Moreover, they utilized a genetic algorithm to select the best model. [PERSON] et al. [12] used the mean phylogenetic distance and taxonomic diversity index to extract pulmonary texture features. After obtaining the texture features, they combined a genetic algorithm and SVM to select the best training model. Finally, their proposed model was tested on the LIDC/IDRI dataset. For traditional pattern recognition methods, manual feature extraction is required, and feature extraction is not easy. A professional radiologist uses a lot of effort to extract the relevant features, and the effectiveness of feature extraction depends on the doctor's expertise in lung cancer diagnosis and on their understanding of traditional machine learning methods. Moreover, the feature extraction process is highly subjective and time-consuming. Therefore, it is very important to develop an algorithm for automatic feature extraction. With the development of deep learning, many researchers utilize deep learning methods to differentiate pulmonary nodules. The common classification network is convolutional neural network (CNN). To classify pulmonary nodules better, researchers usually improve the feature extractor and feature classifier of a CNN network. [PERSON] et al. [13] proposed a Multi-crop convolution neural network (MC-CNN) to automatically extract nodule salient features. They cropped feature maps with different size and concatenated them. The multi-scale feature maps contained more semantic information, and this strategy obtained a good performance on nodule classification. To utilize spatial 3D context information, [PERSON] et al. [14] proposed an end-to-end architecture named dense convolutional binary-tree network to classify nodules. They introduced center-crop operation into DenseNet and utilized separation and fusion operations on transition layers which were useful to enrich multi-scale features. For the above existing deep learning methods on lung cancer diagnosis, the network structure is usually relatively complex and large. Due to the limitation of medical dataset, networks are prone to overfitting and they are insufficient in capturing fine-grained features. Although there are some methods that have addressed these challenges, they were not comprehensive and could be further improved. For example, [PERSON] et al. [15] utilized 3D dual path networks (DPNs) to extract pulmonary nodule volume features which are effective for avoiding overfitting. However, the 3D features of pulmonary nodules are enormous and fine-grained features are not captured effectively. To capture fine-grained features, [PERSON] et al. [16] combined the squeeze-and-excitation attention network with aggregated residual transformations ResNeXt and applied their proposed method to the classification of malignant and benign lung nodules. The squeeze-and-excitation attention network is effective in refining features among channels, but the spatial features are not processed effectively in their work. Based on the DPNs architecture, [PERSON] et al. [17] added the spatial and contextual attention module into DPNs and ensembled a multi-network to classify pulmonary nodules.