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Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.
Automated 3-D intraretinal layer segmentation of macular spectral-domain optical coherence tomography images
Garvin, M.K., Abràmoff, M.D., Wu, X., Russell, S.R., Burns, T.L., Sonka, M.: · 2009
Earlier work this paper cites.
An overview of background modeling for detection of targets and anomalies in hyperspectral remotely sensed imagery
Matteoli, S., Diani, M., Theiler, J.: · 2014
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A review of novelty detection
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2014
Earlier work this paper cites.
Detecting anomalous structures by convolutional sparse models
Carrera, D., Boracchi, G., Foi, A., Wohlberg, B.: · 2015
Earlier work this paper cites.
Automated age-related macular degeneration classification in oct using unsupervised feature learning
Venhuizen, F.G., van Ginneken, B., Bloemen, B., van Grinsven, M.J., Philipsen, R., Hoyng, C., Theelen, T., Sánchez, C.I.: · 2015
Cited alongside, same era.
Predicting semantic descriptions from medical images with convolutional neural networks
Schlegl, T., Waldstein, S.M., Vogl, W.D., Schmidt-Erfurth, U., Langs, G.: · 2015
Cited alongside, same era.
Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E.L., Chintala, S., Fergus, R., et al.: · 2015
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems (2015) Software available from tensorflow.org
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.: · 2015
High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning
Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: · 2016
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Identifying and categorizing anomalies in retinal imaging data
Seeböck, P., Waldstein, S., Klimscha, S., Gerendas, B.S., Donner, R., Schlegl, T., Schmidt-Erfurth, U., Langs, G.: · 2016
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Donahue, J., Krähenbühl, P., Darrell, T.: · 2016
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Semantic image inpainting with perceptual and contextual losses
Yeh, R., Chen, C., Lim, T.Y., Hasegawa-Johnson, M., Do, M.N.: · 2016
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Improved techniques for training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: · 2016
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Cited alongside, same era.
A discriminative framework for anomaly detection in large videos
Del Giorno, A., Bagnell, J.A., Hebert, M.: · 2016
Cited alongside, same era.
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Context encoders: Feature learning by inpainting
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.A.: · 2016
Later among the works it cites.