A self-supervised framework for emphysema anomaly detection and staging in computed tomography scans.

Abstract

Emphysema, a diffuse and heterogeneous phenotype of chronic obstructive pulmonary disease (COPD), carries substantial morbidity and elevates lung cancer risk. While computed tomography (CT) aids in detection and monitoring, current deep learning methods depend on large annotated datasets. Unsupervised anomaly detection (UAD) provides an alternative but faces challenges with emphysema anomalies and weak emphysema semantics. In this study, we propose a self-supervised framework trained exclusively on non-emphysema CT scans using synthetically generated lesions to guide pixel-level anomaly modeling. We introduce EDLNet, an encoder-decoder architecture with spatial-channel refinement and adaptive feature fusion for emphysema detection and localization, followed by an unsupervised manner for emphysema staging. Multi-center evaluations show that our framework outperforms existing UAD approaches in detection and localization, while achieving a mean staging accuracy of 93.13% and a macro AUROC of 99.08%. This approach bridges clinical knowledge and artificial intelligence, offering a scalable and interpretable solution for lung disease analysis.