Dual-domain adversarial learning and feature constraint for unsupervised anomaly detection in chest X-rays

Chest X-ray is the standard screening tool for pulmonary diseases, enabling early detection and timely intervention of lung lesions. Due to scarce and costly abnormal labeling of chest X-rays, unsupervised anomaly detection trained only with normal images has become a major research focus, especially, reconstruction-based methods are favored for learning normal data patterns and making it easier to visualize abnormal regions. Although the current reconstruction-based methods of chest X-rays have achieved good result, they have three problems. Firstly, over-reliance on pixel-level alignment in the spatial domain hinders the capture of global information. Secondly, single-layer feature comparison between original and reconstructed images lacks multi-layer and multi-image analysis, resulting in poor feature consistency. Thirdly, using reconstruction error as anomaly score struggles to balance sensitivity to anomalies and fidelity to normal. Using a reconstruction-based approach trained only on normal images, this study improves rapid screening of thoracic abnormalities and shifts screening criteria toward earlier detection of chest lesions.

We propose an architecture DualA-AD including Dual-domain Adversarial Learning (DAL), Feature Constraint Module (FCM) and Distributed Anomaly Score (DAS). In the training stage, DAL captures image details and global information by integrating spatial and frequency domains for dual-domain adversarial reconstruction. While FCM improves feature consistency between reconstructed and original image by analyzing multi-layer feature difference within individual images and similarity differences across multiple images. In the testing stage, DAS enhances image discriminability by fitting a discriminator's output on the training set as the real distribution and quantifying the deviation of images from this distribution. On three mainstream public chest X-ray datasets, the model was trained using only normal chest X-rays and evaluated on their respective test sets.

In the experiments on three datasets, DualA-AD achieved the best AUC, ACC, and F1 scores against classical, general, and specialized baselines, with AUC improvements over the SOTA of 0.93, 0.77, and 0.48%, respectively.

DualA-AD improves the accuracy for distinguishing normal and abnormal chest X-rays. The method provides an efficient scheme for fast, low-cost intelligent detection of lung abnormalities, with great potential for computer-aided chest radiographic diagnosis and early prevention and management of pulmonary disorders.