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Medical imaging is a domain which suffers from a paucity of manually annotated data for the training of learning algorithms.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of machine learning research , vol. 9, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
C. Ledig, R. A. Heckemann, A. Hammers, J. C. Lopez, V. F. Newcombe, A. Makropoulos, J. Lötjönen, D. K. Menon, and D. Rueckert, “Robust whole-brain segmentation: application to traumatic brain injury,” Medical image analysis , vol. 21, no. 1, pp. 40–58, 2015
2015
Earlier work this paper cites.
S. Hoo-Chang, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning,” IEEE transactions on medical imaging , vol. 35, no. 5, p. 1285, 2016
2016
Earlier work this paper cites.
E. Krivov, M. Pisov, and M. Belyaev, “MRI augmentation via elastic registration for brain lesions segmentation,” in International MICCAI Brainlesion Workshop . Springer, 2017, pp. 369–380
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang et al. , “Photo-realistic single image super-resolution using a generative adversarial network.” in CVPR , vol. 2, no. 3, 2017, p. 4
2017
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv preprint arXiv:1701.07875 , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Madani, M. Moradi, A. Karargyris, and T. Syeda-Mahmood, “Chest x-ray generation and data augmentation for cardiovascular abnormality classification,” in Medical Imaging 2018: Image Processing , vol. 10574. International Society for Optics and Photonics, 2018, p. 105741M
2018
Closest in time.
H. Salehinejad, S. Valaee, T. Dowdell, E. Colak, and J. Barfett, “Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 990–994
2018
Closest in time.
2018
Closest in time.
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2017
Cited alongside, same era.
K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation,” Medical image analysis , vol. 36, pp. 61–78, 2017
2017
Cited alongside, same era.
M. Frid-Adar, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “Synthetic data augmentation using GAN for improved liver lesion classification,” in Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on . IEEE, 2018, pp. 289–293
2018
Cited alongside, same era.
2018
Closest in time.
T. Ross, D. Zimmerer, A. Vemuri, F. Isensee, M. Wiesenfarth, S. Bodenstedt, F. Both, P. Kessler, M. Wagner, B. Müller et al. , “Exploiting the potential of unlabeled endoscopic video data with self-supervised learning,” International journal of computer assisted radiology and surgery , pp. 1–9, 2018
2018
Closest in time.
C. Ledig, A. Schuh, R. Guerrero, R. A. Heckemann, and D. Rueckert, “Structural brain imaging in alzheimer’s disease and mild cognitive impairment: biomarker analysis and shared morphometry database,” Scientific reports , vol. 8, no. 1, p. 11258, 2018
2018
Closest in time.