# Three-Dimensional Multifaceted Attention Encoder-Decoder Networks for Pulmonary Nodule Detection
## 1 Introduction
However, the limited receptive fields of CNNs make establishing long-range dependence on the model challenging. However, long-range dependence can provide information on the location and relationship of the target across the scene, which is crucial for detecting pulmonary nodules. Inspired by the field of natural language processing (NLP), many scholars have fused the transformer module with CNNs to overcome the inherent weakness of CNN structures that cannot establish long-range dependencies. The transformer module weights and maps each token to all other tokens through a self-attention block to obtain the dependencies between different locations [11]. Due to its unique ability to model the global context, it has been widely used in medical images [12]. However, as with traditional CNN structures, the transformer structure itself is inflexible, where each pixel point must interact with all other pixel points, inevitably introducing a portion of redundant information [13]. In particular, in the pulmonary nodule detection task, as pulmonary nodules account for a tiny proportion of the whole CT image, global modeling using the transformer introduces many lung features into the computation (nodule features account for a tiny proportion). In addition, transformer-based models can also require more data to fit than traditional CNN models, and researchers often cannot collect enough data to fit a pure transformer model [14]. In order to establish long-range dependencies with the help of transformer structures under limited datasets, often the following two approaches can be used: 1. training the model on large datasets and applying the feature extraction capabilities obtained from the training to a new task (transfer learning) [15] and 2. using CNN structures instead of positional coding to achieve a more efficient inductive bias [16]. To address the above challenges, this paper introduces local learnable region attention (LRA) [17] into the transformer model. It combines it with traditional CNN models and 3D RPN models to propose the MAED model. LRA can form irregular receptive fields according to the specific conditions of the target and combines with traditional self-attention to form multifaceted attention, enabling the model to identify well irregularly lesioned pulmonary nodules. The multiscale fusion block (MSF) enables the model to better extract pulmonary nodule features of different sizes/diameters. The MAED model utilizes dynamic scaling with cross-entropy loss [18] to cope with the imbalance between positive and negative sample sizes. Overall, the MAED model can extract the texture information of nodules while also establishing efficient long-range dependence, enabling more accurate single-step pulmonary nodule detection. The main points of the contribution of this paper are as follows:  
1. We propose a novel pulmonary nodule detection model, which can change the size of the receptive field according to the characteristics of the detection target and can effectively deal with various irregular lesions of pulmonary nodules. 2. We use CNN structures to optimize the inherent shortcomings of the transformer-based model and enable the MAED model to effectively extract local features of nodules and establish long-range dependencies, improving the reliability and generalization of the model. 3. We evaluate the MAED model on LUNA16 data, and the results of our experiments show that our model achieved both accurate detections of pulmonary nodules and the screening of false positive nodules in 3D CT scans compared to existing methods.