  In medical imaging, obtaining large amounts of labeled data is often a
hurdle, because annotations and pathologies are scarce. Anomaly detection is a
method that is capable of detecting unseen abnormal data while only being
trained on normal (unannotated) data. Several algorithms based on generative
adversarial networks (GANs) exist to perform this task, yet certain limitations
are in place because of the instability of GANs. This paper proposes a new
method by combining an existing method, GANomaly, with progressively growing
GANs. The latter is known to be more stable, considering its ability to
generate high-resolution images. The method is tested using Fashion MNIST,
Medical Out-of-Distribution Analysis Challenge (MOOD), and in-house brain MRI;
using patches of sizes 16x16 and 32x32. Progressive GANomaly outperforms a
one-class SVM or regular GANomaly on Fashion MNIST. Artificial anomalies are
created in MOOD images with varying intensities and diameters. Progressive
GANomaly detected the most anomalies with varying intensity and size.
Additionally, the intermittent reconstructions are proven to be better from
progressive GANomaly. On the in-house brain MRI dataset, regular GANomaly
outperformed the other methods.
