  Lung nodules are commonly missed in chest radiographs. We propose and
evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules
in radiographs. P-AnoGAN modifies the fast anomaly detection generative
adversarial network (f-AnoGAN) by utilizing a progressive GAN and a
convolutional encoder-decoder-encoder pipeline. Model training uses only
unlabelled healthy lung patches extracted from the Indiana University Chest
X-Ray Collection. External validation and testing are performed using healthy
and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for
Radiological Technology datasets, respectively. Our model robustly identifies
patches containing lung nodules in external validation and test data with
ROC-AUC of 91.17% and 87.89%, respectively. These results show unsupervised
methods may be useful in challenging tasks such as lung nodule detection in
radiographs.
