SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI
Baur C, Graf R, Wiestler B, Albarqouni S, Navab N (2020)
Publication Type: Conference contribution
Publication year: 2020
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12262 LNCS
Pages Range: 718-727
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lima, PER
ISBN: 9783030597122
DOI: 10.1007/978-3-030-59713-9_69
Abstract
Recently, it has been shown that CycleGANs are masters of steganography. They cannot only learn reliable mappings between two distributions without calling for paired training data, but can effectively hide information unseen during training in mapping results from which input data can be recovered almost perfectly. When preventing this during training, CycleGANs actually map samples much closer to the training distribution. Here, we propose to leverage this effect in the context of trending unsupervised anomaly detection, which primarily relies on modeling healthy anatomy with generative models. Here, we embed anomaly detection into a CycleGAN-based style-transfer framework, which is trained to translate healthy brain MR images to a simulated distribution with lower entropy and vice versa. By filtering high frequency, low amplitude signals from lower entropy samples during training, the resulting model suppresses anomalies in reconstructions of the input data at test time. Similar to Autoencoder and GAN-based anomaly detection methods, this allows us to delineate pathologies directly from residuals between input and reconstruction. Various ablative studies and comparisons to state-of-the-art methods highlight the potential of our method.
Involved external institutions
Germany (DE)How to cite
APA:
Baur, C., Graf, R., Wiestler, B., Albarqouni, S., & Navab, N. (2020). SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 718-727). Lima, PER: Springer Science and Business Media Deutschland GmbH.
MLA:
Baur, Christoph, et al. "SteGANomaly: Inhibiting CycleGAN Steganography for Unsupervised Anomaly Detection in Brain MRI." Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, Springer Science and Business Media Deutschland GmbH, 2020. 718-727.
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