Three-Dimensional Multifaceted Attention Encoder-Decoder Networks for Pulmonary Nodule Detection

Lung cancer is one of the most dangerous cancers in the world, and its early clinical manifestation is malignant nodules in the lungs, so nodule detection in the lungs can provide the basis for the prevention and treatment of lung cancer. In recent years, the development of neural networks has provided a new paradigm for creating computer-aided systems for pulmonary nodule detection. Currently, the mainstream pulmonary nodule detection models are based on convolutional neural networks (CNN); however, as the output of a CNN is based on a fixed-size convolutional kernel, it can lead to a model that cannot establish an effective long-range dependence and can only model local features of CT images. The self-attention block in the traditional transformer structures, although able to establish long-range dependence, are as ineffective as CNN structures in dealing with irregular lesions of nodules. To overcome these problems, this paper combines the self-attention block with the learnable regional attention block to form the multifaceted attention block, which enables the model to establish a more effective long-term dependence based on the characteristics of pulmonary nodules. And the multifaceted attention block is intermingled with the encoder-decoder structure in the CNN to propose the 3D multifaceted attention encoder-decoder network (MAED), which is able to model CT images locally while establishing effective long-term dependencies. In addition, we design a multiscale module to extract the features of pulmonary nodules at different scales and use a focal loss function to reduce the false alarm rate. We evaluated the proposed model on the large-scale public dataset LUNA16, with an average sensitivity of 89.1% across the seven predefined FPs/scan criteria. The experimental results show that the MAED model is able to simultaneously achieve efficient detection of pulmonary nodules and filtering of false positive nodules.