# Adaptive Aggregated Attention Network for Pulmonary
## 1 Introduction  
Lung cancer is one of the most lethal cancers in the world, posing a threat to people's health [1]. Lung cancer mortality can be significantly reduced through early diagnosis and screening [2]. In the 1960s, computer-aided diagnosis (CAD) was proposed and used to diagnose lung cancer, which relieved the pressure on doctors and helped them to diagnose cases more accurately [3]. Traditionally, researchers manually extracted hand-crafted features and designed classifiers, but designing hand-crafted features was time-consuming and required professional medical knowledge. The effectiveness of feature extraction depends on doctors' expertise in lung cancer diagnosis and their understanding of traditional machine learning methods. Moreover, hand-crafted features were subjective and their generalization was poor. Therefore, it is very important to develop an algorithm for automatic feature extraction. In recent years, end-to-end network has become very popular, and it can automatically extract deep features and learn the internal salient features through iterations. Architectural convolution neural network (CNN) has gained huge success in lung nodule classification. In the early stages, 2D CNN was mainly used for pulmonary nodule classification. Two-dimensional CNN could effectively extract features within an image but could not extract the features between the images which are important for lung nodule classification. In recent years, 3D CNN has led to better performances than 2D CNN [4]. Three-dimensional CNN can extract spatial features of nodules, which are very important in classifying nodules. Although the existing deep learning methods have made great progress in thediagnosis of lung nodules, there are still deficiencies in feature extraction. Due to the small lung cancer dataset, it is difficult to extract rich and useful features. Researchers usually deepen the network depth and width to extract rich features, but networks are prone to overfitting. Moreover, not all of the extracted features are useful and some invalid features will reduce the performance of the model. Therefore, based on the small lung cancer dataset, it is very important to design a novel method which can extract rich and useful fine-grained features automatically, and this method should be able to avoid overfitting as much as possible, so that the robustness of the method can be guaranteed. Attention networks are often effective for capturing fine-grained features. An attention mechanism plays an important role in image classification, and it can make networks capture useful attention features automatically. However, if the attention mechanism is not designed or applied properly, the attention mechanism will filter out important features and focus on some unimportant features instead, which will lead to performance degradation of the model. Although some attention methods have been successfully applied in the field of lung cancer diagnosis, there are still deficiencies in some aspects, such as the multi-scale problem between large nodules and small nodules, and the multi-resolution problem of different nodules. To address these challenges, firstly, we used a 3D dual path network (DPN) as our backbone network, which aggregated the advantages of residual connection and dense connection. Based on the dense connection of a DPN, rich features were extracted and the residual connection avoided overfitting. The backbone network ensured the overall performance of our model. Secondly, we applied the squeeze-and-excitation module (SEN) [5] and residual-attention network (RAN) [6] to extract contextual features and spatial features, and the attention modules were useful to make the network focus on important features and to filter out redundant features. RAN is effective in solving multi-resolution problems because of its bottom-up and top-down structure. Especially, RAN was the first applied method in terms of pulmonary nodule classification.