Abstract
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Here, we perform unsupervised learning to identify anomalies in imaging data as candidates for markers. We propose AnoGAN, a deep convolutional generative adversarial network to learn a manifold of normal anatomical variability, accompanying a novel anomaly scoring scheme based on the mapping from image space to a latent space. Applied to new data, the model labels anomalies, and scores image patches indicating their fit into the learned distribution. Results on optical coherence tomography images of the retina demonstrate that the approach correctly identifies anomalous images, such as images containing retinal fluid or hyperreflective foci.
T. Schlegl—This work has received funding from IBM, FWF (I2714-B31), OeNB (15356, 15929), the Austrian Federal Ministry of Science, Research and Economy (CDL OPTIMA).
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
Notes
- 1.
We adapted: https://github.com/bamos/dcgan-completion.tensorflow.
References
Del Giorno, A., Bagnell, J.A., Hebert, M.: A discriminative framework for anomaly detection in large videos. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 334–349. Springer, Cham (2016). doi:10.1007/978-3-319-46454-1_21
Matteoli, S., Diani, M., Theiler, J.: An overview of background modeling for detection of targets and anomalies in hyperspectral remotely sensed imagery. IEEE J. Selected Top. Appl. Earth Obs. Remote Sens. 7(6), 2317–2336 (2014)
Carrera, D., Boracchi, G., Foi, A., Wohlberg, B.: Detecting anomalous structures by convolutional sparse models. In: 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8 (2015)
Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit. 58, 121–134 (2016)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)
Venhuizen, F.G., van Ginneken, B., Bloemen, B., van Grinsven, M.J., Philipsen, R., Hoyng, C., Theelen, T., Sánchez, C.I.: Automated age-related macular degeneration classification in OCT using unsupervised feature learning. In: SPIE Medical Imaging, International Society for Optics and Photonics, p. 94141I (2015)
Schlegl, T., Waldstein, S.M., Vogl, W.-D., Schmidt-Erfurth, U., Langs, G.: Predicting semantic descriptions from medical images with convolutional neural networks. In: Ourselin, S., Alexander, D.C., Westin, C.-F., Cardoso, M.J. (eds.) IPMI 2015. LNCS, vol. 9123, pp. 437–448. Springer, Cham (2015). doi:10.1007/978-3-319-19992-4_34
Seeböck, P., Waldstein, S., Klimscha, S., Gerendas, B.S., Donner, R., Schlegl, T., Schmidt-Erfurth, U., Langs, G.: Identifying and categorizing anomalies in retinal imaging data. In: NIPS 2016 MLHC Workshop. Preprint arXiv:1612.00686 (2016)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Denton, E.L., Chintala, S., Fergus, R., et al.: Deep generative image models using a Laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, pp. 1486–1494 (2015)
Donahue, J., Krähenbühl, P., Darrell, T.: Adversarial feature learning. arXiv:1605.09782 (2016)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
Yeh, R., Chen, C., Lim, T.Y., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with perceptual and contextual losses. arXiv:1607.07539 (2016)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2226–2234 (2016)
Garvin, M.K., Abrà moff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images. IEEE Trans. Med. Imaging 28(9), 1436–1447 (2009)
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: Feature learning by inpainting. CoRR abs/1604.07379 (2016)
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). Software available from http://www.tensorflow.org
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G. (2017). Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-59050-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59049-3
Online ISBN: 978-3-319-59050-9
eBook Packages: Computer ScienceComputer Science (R0)