A Semi-Supervised Inf-Net Framework for CT-Based Lung Nodule Analysis with a Conceptual Extension Toward Genomic Integration

Abstract

Lung cancer is a primary contributor to cancer-related mortality globally, highlighting the necessity for precise early detection of pulmonary nodules through low-dose CT (LDCT) imaging. Deep learning methods have improved nodule detection and classification; however, their performance is frequently limited by the availability of annotated data and variability among imaging centers. This research presents a CT-driven, semi-supervised framework utilizing the Inf-Net architecture to enhance lung nodule analysis with minimal annotation. The model incorporates multi-scale feature aggregation, Reverse Attention refinement, and pseudo-labeling to efficiently utilize unlabeled CT slices. Experiments conducted on subsets of the LUNA16 dataset indicate that the supervised Inf-Net attains a score of 0.825 on 10,000 labeled slices. In contrast, the semi-supervised variant achieves a score of 0.784 on 20,000 slices that include both labeled and pseudo-labeled data, thus surpassing its supervised baseline of 0.755. This study presents a conceptual framework for the integration of genomic biomarkers with CT-derived features, facilitating the development of future multimodal, biologically informed CAD systems. The proposed semi-supervised Inf-Net framework improves CT-based lung nodule assessment and lays the groundwork for flexible multi-omics diagnostic models.