Multi-Scale Supervised Contrastive Learning for Benign-Malignant Classification of Pulmonary Nodules in Chest Ct Scans

Early identification of malignant pulmonary nodules is of great interest in the lung cancer screening process. However, the surrounding contextual information is usually complex, but could be better preserved in multiple spatial scales. Hence, we propose a multi-scale supervised contrastive learning framework to effectively extract inter-scale and intra-scale contextual information of nodules. First, we employ three hierarchical scales from a 3D CT scan to obtain representations, respectively. Second, a newly designed projection network is used to extract pairwise features and map them to the latent space. Third, a supervised contrastive loss is further applied to pull nodules of same classes closer while push nodules of different classes much more away, which effectively guarantees consistency and also augments performance. Based on 1,226 nodules (benign/malignant: 556/670), our proposed method achieves superior diagnosis performance with an accuracy of 91.8%, and AUC of 96.1%. The proposed method shows its potential for computer-assisted lung cancer diagnosis on CT images.