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.
@article{arxiv.2108.02233,
title = {Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks},
author = {Nitish Bhatt and David Ramon Prados and Nedim Hodzic and Christos Karanassios and H. R. Tizhoosh},
journal= {arXiv preprint arXiv:2108.02233},
year = {2021}
}
Comments
Accepted in EMBC 2021: 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society