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Since the advent of deep convolutional neural networks (DNNs), computer vision has seen an extremely rapid progress that has led to huge advances in medical imaging.
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G. J. S. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. van der Laak, B. van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical image analysis
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P. Isola, J. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017
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J. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in IEEE International Conference on Computer Vision, ICCV 2017, Venice, Italy, October 22-29, 2017
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M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” in Advances in Neural Information Processing Systems 30
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Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, and G. Wang, “Low-dose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss,” IEEE Transactions on Medical Imaging
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C. You, Q. Yang, H. Shan, L. Gjesteby, G. Li, S. Ju, Z. Zhang, Z. Zhao, Y. Zhang, W. Cong, and G. Wang, “Structurally-sensitive multi-scale deep neural network for low-dose CT denoising,” IEEE Access
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X. Yi and P. Babyn, “Sharpness-aware low-dose CT denoising using conditional generative adversarial network,” Journal of Digital Imaging
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I. Sánchez and V. Vilaplana, “Brain MRI super-resolution using generative adversarial networks,” in International Conference on Medical Imaging with Deep Learning
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I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of Wasserstein GANs,” in Advances in Neural Information Processing Systems 30
2017
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H. Chen, Y. Zhang, W. Zhang, P. Liao, K. Li, J. Zhou, and G. Wang, “Low-dose CT via convolutional neural network,” Biomed. Opt. Express
2017
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T. I. Kensuke Umehara, Junko Ota, “Super-resolution imaging of mammograms based on the super-resolution convolutional neural network,” Open Journal of Medical Imaging
2017
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K. Umehara, J. Ota, and T. Ishida, “Application of super-resolution convolutional neural network for enhancing image resolution in chest ct,” J Digit Imaging
2017
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C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi, “Photo-realistic single image super-resolution using a generative adversarial network,” in CVPR
2017
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J. M. Wolterink, A. M. Dinkla, M. H. F. Savenije, P. R. Seevinck, C. A. T. van den Berg, and I. Išgum, “Deep MR to CT synthesis using unpaired data,” in Simulation and Synthesis in Medical Imaging
2017
Cited alongside, same era.
X. Han, “MR-based synthetic CT generation using a deep convolutional neural network method,” Medical Physics
2017
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J. H. Rick Chang, C.-L. Li, B. Poczos, B. V. K. Vijaya Kumar, and A. C. Sankaranarayanan, “One network to solve them all – solving linear inverse problems using deep projection models,” in The IEEE International Conference on Computer Vision (ICCV)
2017
Cited alongside, same era.
M. J. M. Chuquicusma, S. Hussein, J. R. Burt, and U. Bagci, “How to fool radiologists with generative adversarial networks? a visual turing test for lung cancer diagnosis,” 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
2018
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M. Frid-Adar, I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan, “GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification,” Neurocomputing
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C. Bermudez, A. J. Plassard, L. T. Davis, A. T. Newton, S. M. Resnick, and B. A. Landman, “Learning implicit brain MRI manifolds with deep learning,” Proceedings of SPIE–the International Society for Optical Engineering
2018
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A. Madani, M. Moradi, A. Karargyris, and T. Syeda-Mahmood, “Chest x-ray generation and data augmentation for cardiovascular abnormality classification,” 2018
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D. Korkinof, T. Rijken, M. O’Neill, J. Yearsley, H. Harvey, and B. Glocker, “High-resolution mammogram synthesis using progressive generative adversarial networks,” 2018 · 2018
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Y. Hiasa, Y. Otake, M. Takao, T. Matsuoka, K. Takashima, A. Carass, J. Prince, N. Sugano, and Y. Sato, “Cross-modality image synthesis from unpaired data using cycleGAN: Effects of gradient consistency loss and training data size,” in Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
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Z. Zhang, L. Yang, and Y. Zheng, “Translating and segmenting multimodal medical volumes with cycle- and shape-consistency generative adversarial network,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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S. Kida, T. Nakamoto, M. Nakano, K. Nawa, A. Haga, J. Kotoku, H. Yamashita, and K. Nakagawa, “Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network,” Cureus
2018
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D. Ulyanov, A. Vedaldi, and V. S. Lempitsky, “Deep image prior,” in CVPR
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S. Kaji, “Image translation by CNNs trained on unpaired data.” https://github.com/shizuo-kaji/UnpairedImageTranslation , 2019
2019
Closest in time.
S. Kaji, “Image translation for paired image datasets (automap + pix2pix).” https://github.com/shizuo-kaji/PairedImageTranslation , 2019
2019
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X. Yi, “Awesome GAN for medical imaging.” https://github.com/xinario/awesome-gan-for-medical-imaging , 2019
2019
Closest in time.
E. Kang, H. J. Koo, D. H. Yang, J. B. Seo, and J. C. Ye, “Cycle-consistent adversarial denoising network for multiphase coronary CT angiography,” Medical Physics
2019
Closest in time.
T. C. W. Mok and A. C. S. Chung, “Learning data augmentation for brain tumor segmentation with coarse-to-fine generative adversarial networks,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
2019
Closest in time.
A. Ben-Cohen, E. Klang, S. P. Raskin, S. Soffer, S. Ben-Haim, E. Konen, M. M. Amitai, and H. Greenspan, “Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection,” Engineering Applications of Artificial Intelligence
2019
Closest in time.