ECANodule: Accurate Pulmonary Nodule Detection and Segmentation with Efficient Channel Attention.

Accurate detection and segmentation of pulmonary nodules in low-dose CT images is essential for early screening and treatment of lung cancer. Previous methods have often overlooked the critical role of segmentation in nodule feature learning, relying on relatively simple region proposal networks and false positive reduction modules. To address this limitation, we introduce an segmentation branch to fully utilize the additional information such as nodule shape and boundary. Our proposed 3D U-Net detection model based on multi-task learning is optimized through bottom-layer parameter sharing to enhance prediction performance by fully utilizing complementary information between tasks. As for challenging problem of large nodule scale variety and complex background, we add more skip connections between the encoder and decoder structures, enhancing the fusion of features from different levels and facilitating gradient flow, thus reducing model training difficulty. We also incorporate an efficient channel attention module in residual block to improve model learning and representation capability. Our method, named ECANodule, achieves an average detection sensitivity of 91.1% and a segmentation Dice score of 83.4% on the LIDC-IDRI dataset, surpassing many previous detection methods. In addition, we provide in-depth discussions on the multi-task strategy, network structure, and channel attention mechanism, offering valuable insights for future research.