PCOR-net: A prototype-enhanced contrastive ordinal regression network for pneumoconiosis staging

Abstract

Background

Pneumoconiosis 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.

Objective

The 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.

Methods

We 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.

Results

We 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.

Conclusions

PCOR-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.