참고문헌 (41)

1. 10.1007/978-3-540-87650-2_2 Drevelegas, A. & Papanikolaou, N. in Imaging of Brain Tumors with Histological Correlations (ed. Drevelegas, A.) 13-33 (Springer, 2011). 

   

2. IEEE Trans. Med. Imaging BH Menze 34 1993 2015 10.1109/TMI.2014.2377694 Menze, B. H. et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans. Med. Imaging 34, 1993-2024 (2015). 

   상세보기

3. Sci. Data S Bakas 4 2017 10.1038/sdata.2017.117 Bakas, S. et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Sci. Data 4, 170117 (2017). 

   상세보기

4. J. School Psychol. AN Baraldi 48 5 2010 10.1016/j.jsp.2009.10.001 Baraldi, A. N. & Enders, C. K. An introduction to modern missing data analyses. J. School Psychol. 48, 5-37 (2010). 

   상세보기

5. Am. J. Neuroradiol. LN Tanenbaum 38 1103 2017 10.3174/ajnr.A5227 Tanenbaum, L. N. et al. Synthetic MRI for clinical neuroimaging: results of the Magnetic Resonance Image Compilation (MAGiC) prospective, multicenter, multireader trial. Am. J. Neuroradiol. 38, 1103-1110 (2017). 

   상세보기

6. Am. J. Neuroradiol. A Hagiwara 38 257 2017 10.3174/ajnr.A5012 Hagiwara, A. et al. Synthetic MRI in the detection of multiple sclerosis plaques. Am. J. Neuroradiol. 38, 257-263 (2017). 

   상세보기

7. Invest. Radiol. A Hagiwara 52 647 2017 10.1097/RLI.0000000000000365 Hagiwara, A. et al. SyMRI of the brain: rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement. Invest. Radiol. 52, 647 (2017). 

   상세보기

8. Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012) (eds Pereira, F. et al.) 1097-1105 (Neural Information Processing Systems Foundation, 2012). 

9. IEEE Trans. Image Processing K Zhang 26 3142 2017 10.1109/TIP.2017.2662206 Zhang, K., Zuo, W., Chen, Y., Meng, D. & Zhang, L. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Processing 26, 3142-3155 (2017). 

   상세보기

10. IEEE Trans. Pattern Anal. Mach. Intell. C Dong 38 295 2016 10.1109/TPAMI.2015.2439281 Dong, C., Loy, C. C., He, K. & Tang, X. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Mach. Intell. 38, 295-307 (2016). 

    상세보기

11. Xie, J., Xu, L. & Chen, E. Image denoising and inpainting with deep neural networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012) (eds Pereira, F. et al.) 341-349 (Neural Information Processing Systems Foundation, 2012). 

12. 10.1109/CVPR.2009.5206848 Deng, J. et al. ImageNet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248-255 (IEEE, 2009). 

    

13. 10.1109/ICCV.2017.244 Zhu, J.-Y., Park, T., Isola, P. & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In 2017 IEEE International Conference on Computer Vision 2223-2232 (IEEE, 2017). 

    

14. 10.1109/CVPR.2018.00916 Choi, Y. et al. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In 2018 IEEE Conference on Computer Vision and Pattern Recognition 8789-8797 (IEEE, 2018). 

    

15. Goodfellow, I. J. et al. Generative adversarial nets. In Advances in Neural Information Processing Systems 27 (NIPS 2014) (eds Ghahramani, Z. et al.) 2672-2680 (Neural Information Processing Systems Foundation, 2014). 

16. 10.1007/978-3-319-68127-6_2 Wolterink, J. M. et al. Deep MR to CT synthesis using unpaired data. In International Workshop on Simulation and Synthesis in Medical Imaging (eds Tsaftaris, S. et al.) 14-23 (Springer, 2017). 

    

17. IEEE Trans. Med. Imaging SU Dar 38 2375 2019 10.1109/TMI.2019.2901750 Dar, S. U. et al. Image synthesis in multicontrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38, 2375-2388 (2019). 

    상세보기

18. 10.1109/CVPR.2017.632 Isola, P., Zhu, J.-Y., Zhou, T. & Efros, A. A. Image-to-image translation with conditional adversarial networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition 1125-1134 (IEEE, 2017). 

    

19. Liu, M.-Y., Breuel, T. & Kautz, J. Unsupervised image-to-image translation networks. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (eds Guyon, I. et al.) 700-708 (Neural Information Processing Systems Foundation, 2017). 

20. Welander, P., Karlsson, S. & Eklund, A. Generative adversarial networks for image-to-image translation on multicontrast MR images-a comparison of CycleGAN and UNIT. Preprint at https://arxiv.org/abs/1806.07777 (2018). 

21. 10.1007/978-3-030-00889-5_20 Yang, H. et al. Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2018, ML-CDS 2018 (Lecture Notes in Computer Science Vol. 11045) 174-182 (Springer, 2018).. 

    

22. 10.1007/978-3-030-00536-8_4 Hiasa, Y. et al. Cross-modality image synthesis from unpaired data using CycleGAN. In International Workshop on Simulation and Synthesis in Medical Imaging (eds Gooya, A. et al.) 31-41 (Springer, 2018). 

    

23. Am. J. Neuroradiol. A Hagiwara 40 224 2019 10.3174/ajnr.A5927 Hagiwara, A. et al. Improving the quality of synthetic FLAIR images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation. Am. J. Neuroradiol. 40, 224-230 (2019). 

    상세보기

24. 10.1109/CVPR.2019.00259 Lee, D., Kim, J., Moon, W.-J. & Ye, J. C. CollaGAN: collaborative GAN for missing image data imputation. In 2019 IEEE Conference on Computer Vision and Pattern Recognition 2487-2496 (IEEE, 2019). 

    

25. 10.1007/978-3-030-11726-9_28 Myronenko, A. 3D MRI brain tumor segmentation using autoencoder regularization. In International Conference on Medical Image Computing and Computer-Assisted Intervention Brainlesion Workshop (eds Crimi, A. et al.) 311-320 (Springer, 2018). 

    

26. Ecology LR Dice 26 297 1945 10.2307/1932409 Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297-302 (1945). 

    상세보기

27. Salimans, T. et al. Improved techniques for training GANs. In Advances in Neural Information Processing Systems 29 (NIPS 2016) (eds Lee, D. D. et al.) 2234-2242 (Neural Information Processing Systems Foundation, 2016). 

28. 10.1109/CVPR.2017.241 Shrivastava, A. et al. Learning from simulated and unsupervised images through adversarial training. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2107-2116 (IEEE, 2017). 

    

29. 10.1109/ICCV.2017.304 Mao, X. et al. Least squares generative adversarial networks. In 2017 IEEE International Conference on Computer Vision (ICCV) 2813-2821 (IEEE, 2017). 

    

30. Arjovsky, M., Chintala, S. & Bottou, L. Wasserstein GAN. Preprint at https://arxiv.org/abs/1701.07875 (2017). 

31. IEEE Trans. Image Process. Z Wang 13 600 2004 10.1109/TIP.2003.819861 Wang, Z., Bovik, A. C., Sheikh, H. R. & Simoncelli, E. P. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600-612 (2004). 

    상세보기

32. 10.1109/CVPR.2017.19 Ledig, C.et al. Photo-realistic single image super-resolution using a generative adversarial network. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Vol. 2, 4 (IEEE, 2017).. 

    

33. Mathieu, M., Couprie, C. & LeCun, Y. Deep multi-scale video prediction beyond mean square error. Preprint at https://arxiv.org/abs/1511.05440 (2015). 

34. IEEE Trans. Comput. Imaging H Zhao 3 47 2017 10.1109/TCI.2016.2644865 Zhao, H., Gallo, O., Frosio, I. & Kautz, J. Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3, 47-57 (2017). 

    상세보기

35. 10.1007/978-3-319-24574-4_28 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 (eds Navab, N. et al.) 234-241 (Lecture Notes in Computer Science Vol. 9351, Springer, 2015). 

    

36. 10.1109/CVPR.2015.7298594 Szegedy, C. et al. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 1-9 (IEEE, 2015). 

    

37. Chang, S., Park, S., Yang, J. & Kwak, N. Image translation to mixed-domain using sym-parameterized generative network. Preprint at https://arxiv.org/abs/1811.12362 (2018). 

38. Proc. SPIE S Remedios 10575 105752I 2018 Remedios, S., Pham, D. L., Butman, J. A. & Roy, S. Classifying magnetic resonance image modalities with convolutional neural networks. Proc. SPIE 10575, 105752I (2018). 

39. Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors. Preprint at https://arxiv.org/abs/1207.0580 (2012). 

40. J. Mach. Learning Res. N Srivastava 15 1929 2014 Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learning Res. 15, 1929-1958 (2014). 

41. 10.1007/978-3-030-01261-8_1 Wu, Y. & He, K. (2018). Group normalization. In 2018 IEEE European Conference on Computer Vision (ECCV) 3-19 (IEEE, 2018). 

    

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