BackgroundPneumoconiosis is one of the most severe occupational diseases, and accurate staging is essential for treatment planning and disease management. However, the visual features on chest X-rays are often subtle and exhibit gradual transitions between stages, posing challenges for traditional classification models.ObjectiveThe study aims to overcome the limitations of current staging methods, and to develop a model that simultaneously captures the ordinal progression of pneumoconiosis and enhances feature discrimination for reliable staging.MethodsWe propose a Prototype-enhanced Contrastive Ordinal Regression Network (PCOR-Net) for pneumoconiosis staging. PCOR-Net adopts a dual-branch architecture, where a momentum-updated teacher encoder builds dynamic class prototypes, and a student encoder learns more discriminative features under prototype-guided supervision. To capture the ordinal structure of disease progression, we introduce an ordinal-aware prototype contrastive mechanism and a learnable-threshold ordinal regression module that adapts to the non-uniform nature of stage transitions. Three loss functions-prototype contrastive loss, feature distillation loss, and ordinal regression loss-are jointly optimized in a unified framework.ResultsWe conducted experiments on the pneumoconiosis dataset, where PCOR-Net achieved an accuracy of 91.18% and a Quadratic Weighted Kappa (QWK) of 92.72%, outperforming existing state-of-the-art methods. To assess generalizability, PCOR-Net was also evaluated on a COVID-19 severity dataset, demonstrating good transferability.ConclusionsPCOR-Net demonstrates strong effectiveness and robustness in pneumoconiosis staging and generalizes well to the COVID-19 grading dataset, providing reliable support for clinical diagnosis with improved accuracy and ordinal consistency.
Ji et al. (Wed,) studied this question.