Fetching the paper…
Reading the bibliography…
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI.
J. A. Sethian, Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science . Cambridge university press, 1999, vol. 3
1999
Earlier work this paper cites.
A. Taboada-Crispi, H. Sahli, D. Hernandez-Pacheco, and A. Falcon-Ruiz, “Anomaly detection in medical image analysis,” in Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications . IGI Global, 2009, pp. 426–446
2009
Earlier work this paper cites.
T. Rohlfing, N. M. Zahr, E. V. Sullivan, and A. Pfefferbaum, “The SRI24 multichannel atlas of normal adult human brain structure,” Human Brain Mapping , vol. 31, no. 5, pp. 798–819, Dec. 2009
2009
Earlier work this paper cites.
J. E. Iglesias, C.-Y. Liu, P. M. Thompson, and Z. Tu, “Robust Brain Extraction Across Datasets and Comparison With Publicly Available Methods,” IEEE Transactions on Medical Imaging , vol. 30, no. 9, pp. 1617–1634, 2011
2011
Earlier work this paper cites.
2014
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 27 , Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2014, pp. 2672–2680. [Online]. Available: http://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
2014
Earlier work this paper cites.
M. A. Bruno, E. A. Walker, and H. H. Abujudeh, “Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction,” Radiographics , vol. 35, no. 6, pp. 1668–1676, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Y. Xia, X. Cao, F. Wen, G. Hua, and J. Sun, “Learning discriminative reconstructions for unsupervised outlier removal,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 1511–1519
2015
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-Erfurth, and G. Langs, “Unsupervised anomaly detection with generative adversarial networks to guide marker discovery,” in International Conference on Information Processing in Medical Imaging . Springer, 2017, pp. 146–157
2018
Later among the works it cites.
N. Pawlowski, M. C. Lee, M. Rajchl, S. McDonagh, E. Ferrante, K. Kamnitsas, S. Cooke, S. Stevenson, A. Khetani, T. Newman et al. , “Unsupervised lesion detection in brain ct using bayesian convolutional autoencoders,” 2018
2018
Later among the works it cites.
D. Sato, S. Hanaoka, Y. Nomura, T. Takenaga, S. Miki, T. Yoshikawa, N. Hayashi, and O. Abe, “A primitive study on unsupervised anomaly detection with an autoencoder in emergency head ct volumes,” in Medical Imaging 2018: Computer-Aided Diagnosis , vol. 10575. International Society for Optics and Photonics, 2018, p. 105751P
2018
Later among the works it cites.
Ž. Lesjak, A. Galimzianova, A. Koren, M. Lukin, F. Pernuš, B. Likar, and Ž. Špiclin, “A novel public mr image dataset of multiple sclerosis patients with lesion segmentations based on multi-rater consensus,” Neuroinformatics , vol. 16, no. 1, pp. 51–63, 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
A. Carass, S. Roy, A. Jog, J. L. Cuzzocreo, E. Magrath, A. Gherman, J. Button, J. Nguyen, F. Prados, C. H. Sudre et al. , “Longitudinal multiple sclerosis lesion segmentation: resource and challenge,” NeuroImage , vol. 148, pp. 77–102, 2017
2017
Cited alongside, same era.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in Proceedings of the 34th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. International Convention Centre, Sydney, Australia: PMLR, 06–11 Aug 2017, pp. 214–223. [Online]. Available: http://proceedings.mlr.press/v70/arjovsky17a.html
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Later among the works it cites.
H. E. Atlason, A. Love, S. Sigurdsson, V. Gudnason, and L. M. Ellingsen, “Unsupervised brain lesion segmentation from mri using a convolutional autoencoder,” in Medical Imaging 2019: Image Processing , vol. 10949. International Society for Optics and Photonics, 2019, p. 109491H
2019
Later among the works it cites.
D. Zimmerer, F. Isensee, J. Petersen, S. Kohl, and K. Maier-Hein, “Unsupervised anomaly localization using variational auto-encoders,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2019, pp. 289–297
2019
Later among the works it cites.
S. You, K. C. Tezcan, X. Chen, and E. Konukoglu, “Unsupervised lesion detection via image restoration with a normative prior,” in Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning , ser. Proceedings of Machine Learning Research, M. J. Cardoso, A. Feragen, B. Glocker, E. Konukoglu, I. Oguz, G. Unal, and T. Vercauteren, Eds., vol. 102. London, United Kingdom: PMLR, 08–10 Jul 2019, pp. 540–556. [Online]. Available: http://proceedings.mlr.press/v102/you19a.html
2019
Later among the works it cites.
T. Schlegl, P. Seeböck, S. M. Waldstein, G. Langs, and U. Schmidt-Erfurth, “f-anogan: Fast unsupervised anomaly detection with generative adversarial networks,” Medical image analysis , vol. 54, pp. 30–44, 2019
2019
Later among the works it cites.