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Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks
[article]
2021
arXiv
pre-print
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
arXiv:2108.02233v1
fatcat:yrg2i644ffc6dcyxmmshgajr7m