- Thomas Schlegl20,21,
- Philipp Seeböck20,21,
- Sebastian M. Waldstein21,
- Ursula Schmidt-Erfurth21 &
- …
- Georg Langs20
Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 10265))
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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 proposeAnoGAN, 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).
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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. PreprintarXiv: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., Abràmoff, 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. Imaging28(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 fromhttp://www.tensorflow.org
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Authors and Affiliations
Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University Vienna, Vienna, Austria
Thomas Schlegl, Philipp Seeböck & Georg Langs
Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University Vienna, Vienna, Austria
Thomas Schlegl, Philipp Seeböck, Sebastian M. Waldstein & Ursula Schmidt-Erfurth
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University of North Carolina, Chapel Hill, North Carolina, USA
Marc Niethammer
University of North Carolina, Chapel Hill, North Carolina, USA
Martin Styner
Kitware Inc., Carrboro, North Carolina, USA
Stephen Aylward
University of North Carolina, Chapel Hill, North Carolina, USA
Hongtu Zhu
University of Pennsylvania, Philadelphia, Pennsylvania, USA
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University of North Carolina, Chapel Hill, North Carolina, USA
Pew-Thian Yap
University of North Carolina, Chapel Hill, North Carolina, USA
Dinggang Shen
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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
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