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Generative adversarial networks (GANs) are a class of unsupervised machine learning algorithms that can produce realistic images from randomly-sampled vectors in a multi-dimensional space.
Economic evaluation of pet and pet/ct in oncology: evidence and methodologic approaches
Buck, A.K., Herrmann, K., Stargardt, T., Dechow, T., Krause, B.J., Schreyögg, J.: · 2010
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.
Why does synthesized data improve multi-sequence classification?
van Tulder, G., de Bruijne, M.: · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
Earlier work this paper cites.
Hemis: Hetero-modal image segmentation
Havaei, M., Guizard, N., Chapados, N., Bengio, Y.: · 2016
Cited alongside, same era.
Donahue, J., Krähenbühl, P., Darrell, T.: · 2016
Cited alongside, same era.
Medical image synthesis with context-aware generative adversarial networks
Nie, D., Trullo, R., Lian, J., Petitjean, C., Ruan, S., Wang, Q., Shen, D.: · 2017
Cited alongside, same era.
Megapixel size image creation using generative adversarial networks
Marchesi, M.: · 2017
Cited alongside, same era.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., Lehtinen, J.: · 2017
Later among the works it cites.
Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.S., Freymann, J.B., Farahani, K., Davatzikos, C.: · 2017
Later among the works it cites.
Distributed deep learning networks among institutions for medical imaging
Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D.L., Kalpathy-Cramer, J.: · 2018
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The multimodal brain tumor image segmentation benchmark (brats)
Menze, B.H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al.: · 2024
Closest in time.
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