SMDNet: A Pulmonary Nodule Classification Model Based on Positional Self-Supervision and Multi-Direction Attention

Jun Xiong, Chengliang Wang, Xing Wu, Peng Wang, Haidong Wang, Hongqian Wang

Abstract: Accurate classification of pulmonary nodules holds importance in the early diagnosis of lung cancer. Unlike 2D models, 3D models can simultaneously utilize multiple slices as input to capture features. However, 3D models face challenges in capturing nodule features in different directions and discerning feature differences in various positions of computed tomography (CT). We introduce a pulmonary nodule classification model, SMDNet. Firstly, a multi-direction attention is proposed to capture nodule features from sagittal, coronal, and axial axes. Secondly, distinct labels are assigned to the cubes at different cropping positions from CT for binary classification to capture local differences. Besides, gradient boosting decision tree (GBDT) is employed to combine shallow features with deep features to improve accuracy. Comparative experimental results on the largest publicly available dataset of pulmonary nodules, LIDC-IDRI, showed that SMDNet achieves a 4.81% improvement in accuracy under identical data processing.