LDADN: a local discriminant auxiliary disentangled network for key-region-guided chest X-ray image synthesis augmented in pneumoconiosis detection.

Pneumoconiosis is deemed one of China's most common and serious occupational diseases. Its high prevalence and treatment cost create enormous pressure on socio-economic development. However, due to the scarcity of labeled data and class-imbalanced training sets, the computer-aided diagnostic based on chest X-ray (CXR) images of pneumoconiosis remains a challenging task. Current CXR data augmentation solutions cannot sufficiently extract small-scaled features in lesion areas and synthesize high-quality images. Thus, it may cause error detection in the diagnosis phase. In this paper, we propose a local discriminant auxiliary disentangled network (LDADN) to synthesize CXR images and augment in pneumoconiosis detection. This model enables the high-frequency transfer of details by leveraging batches of mutually independent local discriminators. Cooperating with local adversarial learning and the Laplacian filter, the feature in the lesion area can be disentangled by a single network. The results show that LDADN is superior to other compared models in the quantitative assessment metrics. When used for data augmentation, the model synthesized image significantly boosts the performance of the detection accuracy to 99.31%. Furthermore, this study offers beneficial references for insufficient label or class imbalanced medical image data analysis.