Deep Log-Normal Label Distribution Learning for Pneumoconiosis Staging on Chest Radiographs

Abstract

Pneumoconiosis staging has been a challenging task for deep neural networks due to the stage ambiguity in early pneumoconiosis. In this article, we propose a deep log-normal label distribution learning method named DLN-LDL for pneumo-coniosis staging by exploring the intrinsic stage distribution pat-terns of pneumoconiosis. DLN-LDL effectively prevents the deep network from overfitting features in ambiguous chest radiographs that are irrelevant to the stage to which they belong by replacing the one-hot labels with log-normally distributed vectors. The experiments on our collected pneumoconiosis dataset confirm that the proposed DLN-LDL algorithm outperforms other classical methods in terms of Accuracy, Precision, Sensitivity, Specificity, F1-score and Area Under the Curve.