Fetching the paper…
Reading the bibliography…
We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans and the other way around.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., Language models are few-shot learners, in: Proceedings of NeurIPS, 2020, pp. 1877–1901
1901
Earlier work this paper cites.
1901
Earlier work this paper cites.
S. Namasivayam, M. K. Kalra, W. E. Torres, W. C. Small, Adverse reactions to intravenous iodinated contrast media: a primer for radiologists, Emergency Radiology 12 (5) (2006) 210–215
2006
Earlier work this paper cites.
T. Heimann, B. Van Ginneken, M. A. Styner, Y. Arzhaeva, V. Aurich, C. Bauer, A. Beck, C. Becker, R. Beichel, G. Bekes, et al., Comparison and evaluation of methods for liver segmentation from ct datasets, IEEE Transactions on Medical Imaging 28 (8) (2009) 1251–1265
2009
Earlier work this paper cites.
V. Nair, G. E. Hinton, Rectified Linear Units Improve Restricted Boltzmann Machines, in: Proceedings of ICML, 2010, pp. 807–814
2010
Earlier work this paper cites.
I. B. Malone, D. Cash, G. R. Ridgway, D. G. MacManus, S. Ourselin, N. C. Fox, J. M. Schott, MIRIAD—Public release of a multiple time point Alzheimer’s MR imaging dataset, NeuroImage 70 (2013) 33–36
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in: Proceedings of NIPS, Vol. 27, 2014, pp. 2672–2680
2014
Earlier work this paper cites.
J. Sivaswamy, S. Krishnadas, G. D. Joshi, M. Jain, A. U. S. Tabish, Drishti-GS: Retinal image dataset for optic nerve head (ONH) segmentation, in: Proceedings of ISBI, 2014, pp. 53–56
2014
Earlier work this paper cites.
S. Ioffe, C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: Proceedings of ICML, PMLR, 2015, pp. 448–456
2015
Earlier work this paper cites.
D. P. Kingma, J. Ba, Adam: A method for stochastic optimization, in: Proceedings of ICLR, 2015
2015
Earlier work this paper cites.
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, Improved techniques for training GANs, Proceedings of NIPS 29 (2016) 2234–2242
2016
Earlier work this paper cites.
Y. Yan, X. Sun, B. Shen, Contrast agents in dynamic contrast-enhanced magnetic resonance imaging, Oncotarget 8 (26) (2017) 43491–43505
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, A. A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, in: Proceedings of ICCV, 2017, pp. 2223–2232
2017
Earlier work this paper cites.
S. Sabour, N. Frosst, G. E. Hinton, Dynamic Routing between Capsules, in: Proceedings of NIPS, 2017, pp. 3859––3869
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, A. A. Efros, Image-to-image translation with conditional adversarial networks, in: Proceedings of CVPR, 2017, pp. 1125–1134
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, in: Proceedings of NIPS, 2017, pp. 5998–6008
2017
Cited alongside, same era.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. Courville, Improved training of wasserstein gans, in: Proceedings of NIPS, 2017, pp. 5769–5779
2017
Cited alongside, same era.
J. M. Wolterink, T. Leiner, M. A. Viergever, I. Išgum, Generative adversarial networks for noise reduction in low-dose CT, IEEE Transactions on Medical Imaging 36 (12) (2017) 2536–2545
2017
Cited alongside, same era.
J. Krebs, T. Mansi, H. Delingette, L. Zhang, F. C. Ghesu, S. Miao, A. K. Maier, N. Ayache, R. Liao, A. Kamen, Robust non-rigid registration through agent-based action learning, in: Proceedings of MICCAI, 2017, pp. 344–352
2017
Cited alongside, same era.
V. Kearney, B. P. Ziemer, A. Perry, T. Wang, J. W. Chan, L. Ma, O. Morin, S. S. Yom, T. D. Solberg, Attention-aware discrimination for MR-to-CT image translation using cycle-consistent generative adversarial networks, Radiology: Artificial Intelligence 2 (2) (2020) e190027
2020
Later among the works it cites.
Q. Pengjiang, K. Xu, T. Wang, Z. Qiankun, H. Yang, B. Atallah, Z. Junqing, T. Bryan, M. R. F Jr, Estimating CT from MR abdominal images using novel generative adversarial networks, Journal of Grid Computing 18 (2) (2020) 211–226
2020
Later among the works it cites.
L. Chen, X. Yang, G. Jeon, M. Anisetti, K. Liu, A trusted medical image super-resolution method based on feedback adaptive weighted dense network, Artificial Intelligence in Medicine 106 (2020) 101857
2020
Later among the works it cites.
P. Soviany, C. Ardei, R. T. Ionescu, M. Leordeanu, Image difficulty curriculum for generative adversarial networks (CuGAN), in: Proceedings of WACV, 2020, pp. 3463–3472
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
M. M. Rohé, M. Datar, T. Heimann, M. Sermesant, X. Pennec, SVF-Net: Learning deformable image registration using shape matching, in: Proceedings of MICCAI, 2017, pp. 266–274
2017
Cited alongside, same era.
F. Chollet, Xception: Deep learning with depthwise separable convolutions, in: Proceedings of CVPR, 2017, pp. 1251–1258
2017
Cited alongside, same era.
H. Choi, D. S. Lee, Generation of structural MR images from amyloid PET: application to MR-less quantification, Journal of Nuclear Medicine 59 (7) (2018) 1111–1117
2018
Cited alongside, same era.
H. Emami, M. Dong, S. P. Nejad-Davarani, C. K. Glide-Hurst, Generating synthetic cts from magnetic resonance images using generative adversarial networks, Medical Physics 45 (8) (2018) 3627–3636
2018
Cited alongside, same era.
Y. Huo, Z. Xu, S. Bao, C. Bermudez, A. J. Plassard, J. Liu, Y. Yao, A. Assad, R. G. Abramson, B. A. Landman, Splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks, in: Proceedings of SPIE, Vol. 10574, 2018, p. 1057409
2018
Cited alongside, same era.
G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, A. V. Dalca, An unsupervised learning model for deformable medical image registration, in: Proceedings of CVPR, 2018, pp. 9252–9260
2018
Cited alongside, same era.
K. Armanious, C. Jiang, S. Abdulatif, T. Küstner, S. Gatidis, B. Yang, Unsupervised medical image translation using Cycle-MedGAN, in: Proceedings of EUSIPCO, 2019, pp. 1–5
2019
Cited alongside, same era.
J. Kim, M. Kim, H. Kang, K. H. Lee, U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation, in: Proceedings of ICLR, 2019
2019
Cited alongside, same era.
Later among the works it cites.
G. Modanwal, A. Vellal, M. Buda, M. A. Mazurowski, Mri image harmonization using cycle-consistent generative adversarial network, in: Proceedings of SPIE, Vol. 11314, 2020, p. 1131413
2020
Later among the works it cites.
L. Oakden-Rayner, Exploring large-scale public medical image datasets, Academic Radiology 27 (1) (2020) 106–112
2020
Later among the works it cites.
X. Lai, X. Bai, Y. Hao, Unsupervised Generative Adversarial Networks With Cross-Model Weight Transfer Mechanism for Image-to-Image Translation, in: Proceedings of ICCV Workshops, 2021, pp. 1814–1822
2021
Closest in time.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al., An image is worth 16x16 words: Transformers for image recognition at scale, in: Proceedings of ICLR, 2021
2021
Closest in time.
Y. Gao, M. Zhou, D. Metaxas, UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation, in: Proceedings of MICCAI, 2021
2021
Closest in time.
A. Luthra, H. Sulakhe, T. Mittal, A. Iyer, S. Yadav, Eformer: Edge Enhancement based Transformer for Medical Image Denoising, in: Proceedings of ICCV, 2021
2021
Closest in time.
M. Seo, D. Kim, K. Lee, S. Hong, J. S. Bae, J. H. Kim, S. Kwak, Neural Contrast Enhancement of CT Image, in: Proceedings of WCACV, 2021, pp. 3973–3982
2021
Closest in time.
M. Burduja, R. T. Ionescu, Unsupervised Medical Image Alignment with Curriculum Learning, in: Proceedings of ICIP, 2021, pp. 3787–3791
2021
Closest in time.
N. Kiryati, Y. Landau, Dataset growth in medical image analysis research, Journal of Imaging 7 (8) (2021) 155
2021
Closest in time.
A. E. Kavur, N. S. Gezer, M. Barış, S. Aslan, P.-H. Conze, V. Groza, D. D. Pham, S. Chatterjee, P. Ernst, S. Özkan, et al., CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation, Medical Image Analysis 69 (2021) 101950
2021
Closest in time.
T. R. Moen, B. Chen, D. R. Holmes III, X. Duan, Z. Yu, L. Yu, S. Leng, J. G. Fletcher, C. H. McCollough, Low-dose CT image and projection dataset, Medical Physics 48 (2) (2021) 902–911
2021
Closest in time.