2018

GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks

Bowles, Christopher, Chen, Liang, Guerrero, Ricardo et al.

Understand

One of the biggest issues facing the use of machine learning in medical imaging is the lack of availability of large, labelled datasets.

The annotation of medical images is not only expensive and time consuming but also highly dependent on the availability of expert observers.
The limited amount of training data can inhibit the performance of supervised machine learning algorithms which often need very large quantities of data on which to train to avoid overfitting.
So far, much effort has been directed at extracting as much information as possible from what data is available.

Built on

Huppertz, H.J., Wagner, J., Weber, B., House, P., Urbach, H.: Automated quantitative FLAIR analysis in hippocampal sclerosis. Epilepsy research 97
2011
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems. pp. 1097–105 (2012)
2012
Earlier work this paper cites.
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. 2672–80 (2014)
2014
Earlier work this paper cites.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Van Der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: International Conference on Computer Vision. pp. 2758–66 (2015)
2015
Earlier work this paper cites.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Original
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–41. Springer (2015)
2015
Earlier work this paper cites.
Valdés Hernández, M.d.C., Armitage, P.A., Thrippleton, M.J., Chappell, F., Sandeman, E., Muñoz Maniega, S., Shuler, K., Wardlaw, J.M.: Rationale, design and methodology of the image analysis protocol for studies of patients with cerebral small vessel disease and mild stroke. Brain and behavior 5
2015
Earlier work this paper cites.
Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: Ground truth from computer games. In: European Conference on Computer Vision. pp. 102–18. Springer (2016)
2016
Earlier work this paper cites.

Similar

Antoniou, A., Storkey, A., Edwards, H.: Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 (2017)
Original
2017
Cited alongside, same era.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)
Original
2017
Cited alongside, same era.
Kamnitsas, K., Ledig, C., Newcombe, V.F., Simpson, J.P., Kane, A.D., Menon, D.K., Rueckert, D., Glocker, B.: Efficient multi-scale 3d cnn with fully connected crf for accurate brain lesion segmentation. Medical image analysis 36
2017
Cited alongside, same era.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
Original
2017
Cited alongside, same era.
Krivov, E., Pisov, M., Belyaev, M.: MRI augmentation via elastic registration for brain lesions segmentation. In: International MICCAI Brainlesion Workshop. pp. 369–80. Springer (2017)
2017
Cited alongside, same era.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: CVPR. p. 4. No. 3 (2017)
2017
Cited alongside, same era.
Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are GANs created equal? a large-scale study. arXiv preprint arXiv:1711.10337 (2017)
Original
2017
Cited alongside, same era.
Schlegl, T., Seeböck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. arXiv preprint arXiv:1703.05921 (2017)
Original
2017
Cited alongside, same era.

Then

Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: CVPR. p. 5. No. 4 (2017)
2017
Later among the works it cites.
Yeh, R.A., Chen, C., Lim, T.Y., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with deep generative models. In: CVPR. p. 4. No. 3 (2017)
2017
Later among the works it cites.
Zhu, X., Liu, Y., Qin, Z., Li, J.: Data augmentation in emotion classification using generative adversarial networks. arXiv preprint arXiv:1711.00648 (2017)
Original
2017
Later among the works it cites.
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., Rueckert, D.: DRINet for medical image segmentation. IEEE Transactions on Medical Imaging (2018)
2018
Closest in time.
Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., Greenspan, H.: Synthetic data augmentation using GAN for improved liver lesion classification. arXiv preprint arXiv:1801.02385 (2018)
Original
2018
Closest in time.
Guerrero, R., Qin, C., Oktay, O., Bowles, C., Chen, L., Joules, R., Wolz, R., Valdés-Hernández, M., Dickie, D., Wardlaw, J., et al.: White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. Neuroimage: Clinical 17
2018
Closest in time.
Madani, A., Moradi, M., Karargyris, A., Syeda-Mahmood, T.: Chest x-ray generation and data augmentation for cardiovascular abnormality classification. In: Medical Imaging 2018: Image Processing. vol. 10574, p. 105741M. International Society for Optics and Photonics (2018)
2018
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…