A Prior Knowledge-embedded Framework for Occupational Pneumoconiosis Staging

Occupational pneumoconiosis (OP) staging is a standard-oriented image classification process that relies on patients' chest X-rays. However, the pathogenesis of OP affects its staging accuracy from two aspects: a) flocculent pneumoconiosis lesions are difficult to be extracted from X-rays, and b) the stages of OP are often extremely imbalanced, significantly diminishing the effectiveness of classification models that assume a balanced data distribution. These factors lead to inaccurate staging results, and significantly reduce the clinical significance of existing models. To achieve accurate OP staging, we propose a Prior Knowledge-embedded OP staging framework which can enhance OP lesions with a dark channel prior-inspired method and handle imbalanced data with a triple-branch model. Empirical studies on ethically reviewed data sets indicate that the proposed framework performs more accurately in OP staging than state-of-the-art methods.