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This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. Mar, pp. 723–773, 2012
2012
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
Y. Zhu, R. Kiros, R. Zemel, R. Salakhutdinov, R. Urtasun, A. Torralba, and S. Fidler, “Aligning books and movies: Towards story-like visual explanations by watching movies and reading books,” in The IEEE International Conference on Computer Vision (ICCV) , December 2015
2015
Earlier work this paper cites.
I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in neural information processing systems , 2017, pp. 5767–5777
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” in Advances in neural information processing systems , 2017
2017
Earlier work this paper cites.
H. Shan, Y. Zhang, Q. Yang, U. Kruger, M. K. Kalra, L. Sun, W. Cong, and G. Wang, “3-d convolutional encoder-decoder network for low-dose ct via transfer learning from a 2-d trained network,” IEEE transactions on medical imaging , vol. 37, no. 6, pp. 1522–1534, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
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 , vol. 321, pp. 321–331, 2018
2018
Earlier work this paper cites.
H.-C. Shin, N. A. Tenenholtz, J. K. Rogers, C. G. Schwarz, M. L. Senjem, J. L. Gunter, K. P. Andriole, and M. Michalski, “Medical image synthesis for data augmentation and anonymization using generative adversarial networks,” in International workshop on simulation and synthesis in medical imaging . Springer, 2018, pp. 1–11
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
G. Kwon, C. Han, and D.-s. Kim, “Generation of 3d brain mri using auto-encoding generative adversarial networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2019, pp. 118–126
2019
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
W. Yan, Y. Wang, S. Gu, L. Huang, F. Yan, L. Xia, and Q. Tao, “The domain shift problem of medical image segmentation and vendor-adaptation by unet-gan,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22 . Springer, 2019, pp. 623–631
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Hofmanninger, F. Prayer, J. Pan, S. Röhrich, H. Prosch, and G. Langs, “Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem,” European Radiology Experimental , vol. 4, no. 1, pp. 1–13, 2020
2020
Earlier work this paper cites.
X. Xie, J. Chen, Y. Li, L. Shen, K. Ma, and Y. Zheng, “Mi 2 gan: Generative adversarial network for medical image domain adaptation using mutual information constraint,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 516–525
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
S. Xing, H. Sinha, and S. J. Hwang, “Cycle consistent embedding of 3d brains with auto-encoding generative adversarial networks,” in Medical Imaging with Deep Learning , 2021
2021
Cited alongside, same era.
P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 12 873–12 883
2021
Cited alongside, same era.
W. H. Pinaya, P.-D. Tudosiu, J. Dafflon, P. F. Da Costa, V. Fernandez, P. Nachev, S. Ourselin, and M. J. Cardoso, “Brain imaging generation with latent diffusion models,” in MICCAI Workshop on Deep Generative Models . Springer, 2022, pp. 117–126
2022
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2022
Later among the works it cites.
2022
Later among the works it cites.
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R. L. Draelos, D. Dov, M. A. Mazurowski, J. Y. Lo, R. Henao, G. D. Rubin, and L. Carin, “Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes,” Medical image analysis , vol. 67, p. 101857, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
H. Montenegro, W. Silva, and J. S. Cardoso, “Privacy-preserving generative adversarial network for case-based explainability in medical image analysis,” IEEE Access , vol. 9, pp. 148 037–148 047, 2021
2021
Cited alongside, same era.
D. Mahapatra, A. Poellinger, L. Shao, and M. Reyes, “Interpretability-driven sample selection using self supervised learning for disease classification and segmentation,” IEEE transactions on medical imaging , vol. 40, no. 10, pp. 2548–2562, 2021
2021
Cited alongside, same era.
D. Li, J. Yang, K. Kreis, A. Torralba, and S. Fidler, “Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 8300–8311
2021
Cited alongside, same era.
2021
Cited alongside, same era.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi, “Photorealistic text-to-image diffusion models with deep language understanding,” 2022
2022
Cited alongside, same era.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 684–10 695
2022
Cited alongside, same era.
2022
Later among the works it cites.
C. Mauri, S. Cerri, O. Puonti, M. Mühlau, and K. Van Leemput, “Accurate and explainable image-based prediction using a lightweight generative model,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2022, pp. 448–458
2022
Later among the works it cites.
W. Peng, E. Adeli, T. Bosschieter, S. H. Park, Q. Zhao, and K. M. Pohl, “Generating realistic brain mris via a conditional diffusion probabilistic model,” 2023
2023
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J. S. Yoon, C. Zhang, H.-I. Suk, J. Guo, and X. Li, “SADM: Sequence-aware diffusion model for longitudinal medical image generation,” in Lecture Notes in Computer Science . Springer Nature Switzerland, 2023, pp. 388–400
2023
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K. Han, Y. Xiong, C. You, P. Khosravi, S. Sun, X. Yan, J. Duncan, and X. Xie, “Medgen3d: A deep generative framework for paired 3d image and mask generation,” 2023
2023
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F. Khader, G. Mueller-Franzes, S. T. Arasteh, T. Han et al. , “Denoising diffusion probabilistic models for 3d medical image generation,” Scientific Reports , vol. 13, no. 7303, 2023
2023
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L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” 2023
2023
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S. Lin and X. Yang, “Diffusion model with perceptual loss,” arXiv preprint arXiv:2401.00110 , 2023
2023
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J. Wasserthal, H.-C. Breit, M. T. Meyer, M. Pradella, D. Hinck, A. W. Sauter, T. Heye, D. T. Boll, J. Cyriac, S. Yang, M. Bach, and M. Segeroth, “Totalsegmentator: Robust segmentation of 104 anatomic structures in ct images,” Radiology: Artificial Intelligence , vol. 5, no. 5, p. e230024, 2023
2023
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S. Singla, M. Eslami, B. Pollack, S. Wallace, and K. Batmanghelich, “Explaining the black-box smoothly—a counterfactual approach,” Medical Image Analysis , vol. 84, p. 102721, 2023
2023
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