Fetching the paper…
Reading the bibliography…
Multi-modal medical images provide complementary soft-tissue characteristics that aid in the screening and diagnosis of diseases.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th annual international conference on machine learning , 2009, pp. 41–48
2009
Earlier work this paper cites.
S. Roy, A. Carass, N. Shiee, D. L. Pham, and J. L. Prince, “Mr contrast synthesis for lesion segmentation,” in 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro . IEEE, 2010, pp. 932–935
2010
Earlier work this paper cites.
J. E. Iglesias, E. Konukoglu, D. Zikic, B. Glocker, K. Van Leemput, and B. Fischl, “Is synthesizing mri contrast useful for inter-modality analysis?” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I 16 . Springer, 2013, pp. 631–638
2013
Earlier work this paper cites.
S. Roy, A. Carass, and J. L. Prince, “Magnetic resonance image example-based contrast synthesis,” IEEE transactions on medical imaging , vol. 32, no. 12, pp. 2348–2363, 2013
2013
Earlier work this paper cites.
D. H. Ye, D. Zikic, B. Glocker, A. Criminisi, and E. Konukoglu, “Modality propagation: coherent synthesis of subject-specific scans with data-driven regularization,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I 16 . Springer, 2013, pp. 606–613
2013
Earlier work this paper cites.
R. Li et al. , “Deep learning based imaging data completion for improved brain disease diagnosis,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part III 17 . Springer, 2014, pp. 305–312
2014
Earlier work this paper cites.
A. Jog, A. Carass, D. L. Pham, and J. L. Prince, “Random forest flair reconstruction from t 1, t 2, and p d-weighted mri,” in 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) . IEEE, 2014, pp. 1079–1082
2014
Earlier work this paper cites.
B. H. Menze et al. , “The multimodal brain tumor image segmentation benchmark (brats),” IEEE transactions on medical imaging , vol. 34, no. 10, pp. 1993–2024, 2014
2014
Earlier work this paper cites.
A. Jog, A. Carass, S. Roy, D. L. Pham, and J. L. Prince, “Mr image synthesis by contrast learning on neighborhood ensembles,” Medical image analysis , vol. 24, no. 1, pp. 63–76, 2015
2015
Earlier work this paper cites.
R. Vemulapalli, H. Van Nguyen, and S. K. Zhou, “Unsupervised cross-modal synthesis of subject-specific scans,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 630–638
2015
Earlier work this paper cites.
H. Van Nguyen, K. Zhou, and R. Vemulapalli, “Cross-domain synthesis of medical images using efficient location-sensitive deep network,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I 18 . Springer, 2015, pp. 677–684
2015
Earlier work this paper cites.
C. Bowles et al. , “Pseudo-healthy image synthesis for white matter lesion segmentation,” in Simulation and Synthesis in Medical Imaging: First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings 1 . Springer, 2016, pp. 87–96
2016
Earlier work this paper cites.
S. Roy, Y.-Y. Chou, A. Jog, J. A. Butman, and D. L. Pham, “Patch based synthesis of whole head mr images: application to epi distortion correction,” in Simulation and Synthesis in Medical Imaging: First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings 1 . Springer, 2016, pp. 146–156
2016
Earlier work this paper cites.
Y. Huang, L. Beltrachini, L. Shao, and A. F. Frangi, “Geometry regularized joint dictionary learning for cross-modality image synthesis in magnetic resonance imaging,” in Simulation and Synthesis in Medical Imaging: First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings 1 . Springer, 2016, pp. 118–126
2016
Earlier work this paper cites.
V. Sevetlidis, M. V. Giuffrida, and S. A. Tsaftaris, “Whole image synthesis using a deep encoder-decoder network,” in Simulation and Synthesis in Medical Imaging: First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings 1 . Springer, 2016, pp. 127–137
2016
Earlier work this paper cites.
M. Havaei, N. Guizard, N. Chapados, and Y. Bengio, “Hemis: Hetero-modal image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19 . Springer, 2016, pp. 469–477
2016
Earlier work this paper cites.
D. Nie et al. , “Medical image synthesis with context-aware generative adversarial networks,” in Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20 . Springer, 2017, pp. 417–425
2017
Earlier work this paper cites.
A. Jog, A. Carass, S. Roy, D. L. Pham, and J. L. Prince, “Random forest regression for magnetic resonance image synthesis,” Medical image analysis , vol. 35, pp. 475–488, 2017
2017
Earlier work this paper cites.
A. Chartsias, T. Joyce, M. V. Giuffrida, and S. A. Tsaftaris, “Multimodal mr synthesis via modality-invariant latent representation,” IEEE transactions on medical imaging , vol. 37, no. 3, pp. 803–814, 2017
2017
Earlier work this paper cites.
S. Bakas et al. , “Advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features,” Scientific data , vol. 4, no. 1, pp. 1–13, 2017
2017
Earlier work this paper cites.
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley, “Least squares generative adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2794–2802
2017
Cited alongside, same era.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 1125–1134
2017
Cited alongside, same era.
Y. Pan, M. Liu, C. Lian, T. Zhou, Y. Xia, and D. Shen, “Synthesizing missing pet from mri with cycle-consistent generative adversarial networks for alzheimer’s disease diagnosis,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part III 11 . Springer, 2018, pp. 455–463
2018
Cited alongside, same era.
——, “Medical image synthesis with deep convolutional adversarial networks,” IEEE Transactions on Biomedical Engineering , vol. 65, no. 12, pp. 2720–2730, 2018
V. E. Staartjes, P. R. Seevinck, W. P. Vandertop, M. van Stralen, and M. L. Schröder, “Magnetic resonance imaging–based synthetic computed tomography of the lumbar spine for surgical planning: a clinical proof-of-concept,” Neurosurgical focus , vol. 50, no. 1, p. E13, 2021
2021
Later among the works it cites.
Q. Wang et al. , “Realistic lung nodule synthesis with multi-target co-guided adversarial mechanism,” IEEE Transactions on Medical Imaging , vol. 40, no. 9, pp. 2343–2353, 2021
2021
Later among the works it cites.
B. Peng, B. Liu, Y. Bin, L. Shen, and J. Lei, “Multi-modality mr image synthesis via confidence-guided aggregation and cross-modality refinement,” IEEE Journal of Biomedical and Health Informatics , vol. 26, no. 1, pp. 27–35, 2021
2021
Later among the works it cites.
M. Yurt, S. U. Dar, A. Erdem, E. Erdem, K. K. Oguz, and T. Çukur, “mustgan: multi-stream generative adversarial networks for mr image synthesis,” Medical image analysis , vol. 70, p. 101944, 2021
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
S. Olut, Y. H. Sahin, U. Demir, and G. Unal, “Generative adversarial training for mra image synthesis using multi-contrast mri,” in PRedictive Intelligence in MEdicine: First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 1 . Springer, 2018, pp. 147–154
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
S. U. Dar, M. Yurt, L. Karacan, A. Erdem, E. Erdem, and T. Cukur, “Image synthesis in multi-contrast mri with conditional generative adversarial networks,” IEEE transactions on medical imaging , vol. 38, no. 10, pp. 2375–2388, 2019
2019
Cited alongside, same era.
D. Lee, J. Kim, W.-J. Moon, and J. C. Ye, “Collagan: Collaborative gan for missing image data imputation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2487–2496
2019
Cited alongside, same era.
H. Li et al. , “Diamondgan: unified multi-modal generative adversarial networks for mri sequences synthesis,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part IV 22 . Springer, 2019, pp. 795–803
2019
Cited alongside, same era.
X. Yang, Y. Lin, Z. Wang, X. Li, and K.-T. Cheng, “Bi-modality medical image synthesis using semi-supervised sequential generative adversarial networks,” IEEE journal of biomedical and health informatics , vol. 24, no. 3, pp. 855–865, 2019
2019
Cited alongside, same era.
A. Hagiwara et al. , “Improving the quality of synthetic flair images with deep learning using a conditional generative adversarial network for pixel-by-pixel image translation,” American journal of neuroradiology , vol. 40, no. 2, pp. 224–230, 2019
2019
Cited alongside, same era.
Later among the works it cites.
T. Zhou, S. Canu, P. Vera, and S. Ruan, “Latent correlation representation learning for brain tumor segmentation with missing mri modalities,” IEEE Transactions on Image Processing , vol. 30, pp. 4263–4274, 2021
2021
Later among the works it cites.
J. Gou, B. Yu, S. J. Maybank, and D. Tao, “Knowledge distillation: A survey,” International Journal of Computer Vision , vol. 129, no. 6, pp. 1789–1819, 2021
2021
Later among the works it cites.
Y. Luo et al. , “Adaptive rectification based adversarial network with spectrum constraint for high-quality pet image synthesis,” Medical Image Analysis , vol. 77, p. 102335, 2022
2022
Later among the works it cites.
Y. Fei et al. , “Classification-aided high-quality pet image synthesis via bidirectional contrastive gan with shared information maximization,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 527–537
2022
Later among the works it cites.
O. Dalmaz, M. Yurt, and T. Çukur, “Resvit: residual vision transformers for multimodal medical image synthesis,” IEEE Transactions on Medical Imaging , vol. 41, no. 10, pp. 2598–2614, 2022
2022
Later among the works it cites.
X. Liu, J. Wang, C. Peng, S. S. Chandra, F. Liu, and S. K. Zhou, “Undersampled mri reconstruction with side information-guided normalisation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 323–333
2022
Later among the works it cites.
K. Han et al. , “A survey on vision transformer,” IEEE transactions on pattern analysis and machine intelligence , vol. 45, no. 1, pp. 87–110, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Closest in time.
M. Özbey et al. , “Unsupervised medical image translation with adversarial diffusion models,” IEEE Transactions on Medical Imaging , 2023
2023
Closest in time.
J. Liu, S. Pasumarthi, B. Duffy, E. Gong, K. Datta, and G. Zaharchuk, “One model to synthesize them all: Multi-contrast multi-scale transformer for missing data imputation,” IEEE Transactions on Medical Imaging , 2023
2023
Closest in time.
O. Dalmaz et al. , “One model to unite them all: Personalized federated learning of multi-contrast mri synthesis,” Medical Image Analysis , p. 103121, 2024
2024
Closest in time.
P. Tiwary, K. Bhattacharyya, and A. Prathosh, “Cycle consistent twin energy-based models for image-to-image translation,” Medical Image Analysis , vol. 91, p. 103031, 2024
2024
Closest in time.
M. F. Chaudhary et al. , “Lungvit: Ensembling cascade of texture sensitive hierarchical vision transformers for cross-volume chest ct image-to-image translation,” IEEE transactions on medical imaging , 2024
2024
Closest in time.
Z. Wang et al. , “Mutual information guided diffusion for zero-shot cross-modality medical image translation,” IEEE Transactions on Medical Imaging , 2024
2024
Closest in time.
H. Hao, J. Xue, P. Huang, L. Ren, and D. Li, “Qgformer: Queries-guided transformer for flexible medical image synthesis with domain missing,” Expert Systems with Applications , vol. 247, p. 123318, 2024
2024
Closest in time.