Accurate pneumoconiosis staging via deep texture encoding and discriminative representation learning

Abstract

Accurate pneumoconiosis staging is key to early intervention and treatment planning for pneumoconiosis patients. The staging process relies on assessing the profusion level of small opacities, which are dispersed throughout the entire lung field and manifest as fine textures. While conventional convolutional neural networks (CNNs) have achieved significant success in tasks such as image classification and object recognition, they are less effective for classifying fine-grained medical images due to the need for global, orderless feature representation. This limitation often results in inaccurate staging outcomes for pneumoconiosis. In this study, we propose a deep texture encoding scheme with a suppression strategy designed to capture the global, orderless characteristics of pneumoconiosis lesions while suppressing prominent regions such as the ribs and clavicles within the lung field. To further enhance staging accuracy, we incorporate an ordinal label distribution to capture the ordinal information among profusion levels of opacities. Additionally, we employ supervised contrastive learning to develop a more discriminative feature space for downstream classification tasks. Finally, in accordance with standard practices, we evaluate the profusion levels of opacities in each subregion of the lung, rather than relying on the entire chest X-ray image. Experimental results on the pneumoconiosis dataset demonstrate the superior performance of the proposed method confirming its effectiveness for accurate pneumoconiosis staging.

Keywords: chest X-ray; deep texture encoding; label distribution learning; pneumoconiosis staging; supervised contrastive learning.

Figure 1

Examples of pneumoconiosis stage.

Figure 2

Example of subdivision of the lung fields.

Figure 3

The overall framework of the proposed method.

Figure 4

Visualization of feature representation distribution on data case C1. (A) w/o supervised contrastive learning. (B) w/ supervised contrastive learning.

Figure 5

Accuracy with different suppression factors on data case C1.

Figure 6

Confusion matrixes on data case C1. (A) Chollet (29). (B) Huang et al. (28). (C) Wang et al. (14). (D) Yang et al. (4). (E) Zhang et al. (15). (F) Ours.