# Three-Dimensional Multifaceted Attention Encoder-Decoder Networks for Pulmonary Nodule Detection
## 1 Introduction  
According to data published by the World Health Organization's Agency for Research on Cancer, in 2020 alone, 2.21 million people suffered from lung cancer and 1.8 million people died from lung cancer. Lung cancer is the second most common cancer globally and the number one cause of death. Lung cancer has no apparent complications in its early stages, so many patients are diagnosed with advanced lung cancer and miss out on the best time to cure it. The early clinical manifestation of lung cancer is a malignant nodule in the lung, so early detection and effective treatment of pulmonary nodules can significantly improve the survival rate of patients [1]. Quantitative CT imaging of the lungs can detect potential lung cancer patients better than standard chest X-ray screening [2], making CT images one of the main tools to assist physicians in diagnosing pulmonary nodules. However, radiologists must look at hundreds or even thousands of CT slices for each patient to reach an accurate conclusion. Due to the physician's ability, work experience, and work status limitations, the results are oftennot objective enough and are prone to misses and misdiagnoses. Many researchers have therefore worked to develop efficient computer-aided detection systems to achieve accurate and effective detection of pulmonary nodules in low-dose CT scans. Early computer-aided diagnostic systems, such as morphological thresholds, relied on a priori knowledge. They required manual extraction of low-level features of pulmonary nodules, making it challenging to discriminate complex nodules after specific lesions have occurred [3]. According to [PERSON] et al. [4], AI can improve the knowledge of healthcare professionals, enabling them to spend more time on direct patient care and reduce fatigue. In recent years, deep learning of AI has demonstrated powerful learning capabilities in processing raw image tasks. The advantage of deep learning-based models is that they can learn the intrinsic representation of the data despite the absence of prior knowledge. There are many deep learning-based approaches to achieve pulmonary nodule detection [5], primarily based on a two-stage strategy: 1. candidate pulmonary nodule detection and 2. false positive pulmonary nodule filtering. For example, [PERSON] et al. [6] combined multiscale blocks with Faster R-CNN blocks to construct a pulmonary nodule detection model and proposed a 3D convolution-based MSM-CNN model to filter false positive pulmonary nodules. [PERSON] et al. [7] incorporated a multiscale attention block into an encoder-decoder network as the backbone of a Faster R-CNN model for pulmonary nodule detection. And the 3D multiscale attention block was incorporated into the Res2 Net structure for a false positive reduction in pulmonary nodules. [PERSON] et al. [8] combined Res2 Net and the channel self-attention mechanism as the backbone of the model for the detection of lungs and the false positive reduction in nodules. Although the same network structure is adopted for different tasks, the features that the two networks advance for each other do not share with each other and still need to be trained multiple times. For multi-stage network models, features are extracted from the original dataset and predicted at each stage. However, the repetitive feature extraction will bring much computational cost [9]. Therefore, many scholars have begun constructing single-stage models for detecting lung nodules. For example, [PERSON] et al. [10] designed a feature extraction network with an encoding-decoding structure. They combined it with a 3D region suggestion network (RPN) to effectively detect pulmonary nodules. The single-stage pulmonary nodule detection task, without the false positive nodule screening component, necessitates that the network has a more robust feature extraction capability to identify pulmonary nodules more accurately. Most feature extraction networks are based on convolutional neural networks (CNNs).