Abstract
In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. The usage of deep neural networks for novelty detection remains an open challenge. Here we propose novelty detection methods based on training variational autoencoders (VAEs) on normal data. We apply these methods to magnetic resonance imaging, namely to the detection of diffusion-space (q-space) abnormalities in diffusion MRI scans of multiple sclerosis patients. q-Space novelty detection can reduce scan time duration and does not require any disease-specific prior knowledge, thus overcoming the disadvantages of other diffusion MRI processing methods. The methods proposed herein outperform the state of the art on q-space data in terms of quality and inference time. Our methods also outperform the state of the art on a standard novelty detection benchmark, and hence are also promising for non-MRI novelty detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
An, J., Cho, S.: Variational Autoencoder based Anomaly Detection using Reconstruction Probability. Technical Report, SNU Data Mining Center (2015)
Goodfellow, I.J., et al.: Generative Adversarial Nets. NIPS 2672–2680 (2014)
Golkov, V., et al.: q-space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans. Med. Imaging 35(5), 1344–1351 (2016)
Golkov, V., et al.: Model-Free Novelty-Based Diffusion MRI (2016). ISBI 1233-1236
Golkov, V., et al.: q-Space Novelty Detection in Short Diffusion MRI Scans of Multiple Sclerosis. ISMRM 5378 (2018)
Golkov, V., et al.: q-Space Deep Learning for Alzheimer’s Disease Diagnosis: Global Prediction and Weakly-Supervised Localization. ISMRM 1580 (2018)
Hoffmann, H.: Kernel PCA for novelty detection. Pattern Recognit. 40(3), 863–874 (2007)
Kingma, D.P., Adam, J.Ba.: A Method for Stochastic Optimization. ICLR (2014)
Kingma, D.P., Welling, M.: Auto-Encoding Variational Bayes. ICLR (2014)
Klein, S., et al.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)
Leveau, V., Joly, A.: Adversarial Autoencoders for Novelty Detection. Research Report, Inria—Sophia Antipolis (2017)
Makhzani, A., et al.: Adversarial Autoencoders. ICLR (2016)
Nocedal, J.: Updating quasi-newton matrices with limited storage. Math. Comput. 35(151), 773–782 (1980)
Pimentel, M.A.F., et al.: A review of novelty detection. Signal Process. 99, 215–249 (2014)
Rezende, D.J., et al.: Stochastic backpropagation and approximate inference in deep generative models. ICML 32(2), 1278–1286 (2014)
Schlegl, T., et al.: Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. IPMI (2017)
Swazinna, P., et al.: Negative-Unlabeled Learning for Diffusion MRI. ISMRM 3480 (2019)
Wang, H., et al.: Generative adversarial network based novelty detection using minimized reconstruction error. Frontiers Inf. Technol. Electron. Eng. 19, 116 (2018)
Wang, W., et al.: Safer Classification by Synthesis (2017). arXiv:1711.08534
Zong, B., et al.: Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection. ICLR (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Vasilev, A. et al. (2020). q-Space Novelty Detection with Variational Autoencoders. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-52893-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52892-8
Online ISBN: 978-3-030-52893-5
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)