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To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training.
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Generative adversarial nets
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Wm-dova maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians
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Synthetic data for text localisation in natural images
A. Gupta, A. Vedaldi, and A. Zisserman · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Learning depth from single monocular images using deep convolutional neural fields
F. Liu, C. Shen, G. Lin, and I. Reid · 2016
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Computer-aided detection of polyps in optical colonoscopy images
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P. Costa, A. Galdran, M. I. Meyer, M. Niemeijer, M. Abràmoff, A. M. Mendonça, and A. Campilho · 2017
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J. T. Guibas, T. S. Virdi, and P. S. Li · 2017
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Adversarial networks for the detection of aggressive prostate cancer
S. Kohl, D. Bonekamp, H.-P. Schlemmer, K. Yaqubi, M. Hohenfellner, B. Hadaschik, J.-P. Radtke, and K. Maier-Hein · 2017
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Deep generative adversarial networks for compressed sensing automates mri
M. Mardani, E. Gong, J. Y. Cheng, S. Vasanawala, G. Zaharchuk, M. Alley, N. Thakur, S. Han, W. Dally, J. M. Pauly, et al · 2017
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Adversarial training and dilated convolutions for brain mri segmentation
P. Moeskops, M. Veta, M. W. Lafarge, K. A. Eppenhof, and J. P. Pluim · 2017
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Learning from simulated and unsupervised images through adversarial training
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Automated polyp detection in colonoscopy videos using shape and context information
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Generative visual manipulation on the natural image manifold
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GANs for biological image synthesis
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Generative adversarial networks for noise reduction in low-dose ct
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Semi-supervised assessment of incomplete lv coverage in cardiac mri using generative adversarial nets
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Deep adversarial networks for biomedical image segmentation utilizing unannotated images
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