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Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data.
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A. M. Delaney, E. Brophy, and T. E. Ward, “Synthesis of realistic ecg using generative adversarial networks,” arXiv preprint 1909.09150 , 2019
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T. Golany and K. Radinsky, “Pgans: Personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, pp. 557–564, Jul. 2019
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T. Shadbahr, M. Roberts, J. Stanczuk, J. Gilbey, P. Teare, S. Dittmer, M. Thorpe, R. V. Torne, E. Sala, P. Lio, M. Patel, A.-C. Collaboration, J. H. F. Rudd, T. Mirtti, A. Rannikko, J. A. D. Aston, J. Tang, and C.-B. Schönlieb, “Classification of datasets with imputed missing values: does imputation quality matter?” arXiv preprint 2206.08478 , 2022
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T. Mehari and N. Strodthoff, “Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models,” in arXiv , 2022, extended abstract
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N. Chen, Y. Zhang, H. Zen, R. J. Weiss, M. Norouzi, and W. Chan, “Wavegrad: Estimating gradients for waveform generation,” in International Conference on Learning Representations , 2020
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J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 6840–6851
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T. Golany, G. Lavee, S. Tejman Yarden, and K. Radinsky, “Improving ecg classification using generative adversarial networks,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 08, pp. 13 280–13 285, Apr. 2020
2020
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T. Golany, K. Radinsky, and D. Freedman, “SimGANs: Simulator-based generative adversarial networks for ECG synthesis to improve deep ECG classification,” in Proceedings of the 37th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 3597–3606
2020
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
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R. Kumar, W. Wang, J. Kumar, T. Yang, A. Khan, W. Ali, and I. Ali, “An integration of blockchain and ai for secure data sharing and detection of ct images for the hospitals,” Computerized Medical Imaging and Graphics , vol. 87, p. 101812, 2021
2021
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H. Yin, A. Mallya, A. Vahdat, J. M. Alvarez, J. Kautz, and P. Molchanov, “See through gradients: Image batch recovery via gradinversion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 16 337–16 346
2021
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2022
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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
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J. Ho, T. Salimans, A. Gritsenko, W. Chan, M. Norouzi, and D. J. Fleet, “Video diffusion models,” arXiv preprint 2204.03458 , 2022
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J. M. L. Alcaraz and N. Strodthoff, “SSSD-ECG public code repository,” https://zenodo.org/account/settings/github/repository/AI4HealthUOL/SSSD-ECG , accessed: 2022-12-31
2022
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A. Alaa, B. Van Breugel, E. S. Saveliev, and M. van der Schaar, “How faithful is your synthetic data? Sample-level metrics for evaluating and auditing generative models,” in Proceedings of the 39th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, Eds., vol. 162. PMLR, 17–23 Jul 2022, pp. 290–306
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K. Falahkheirkhah, S. Tiwari, K. Yeh, S. Gupta, L. Herrera-Hernandez, M. R. McCarthy, R. E. Jimenez, J. C. Cheville, and R. Bhargava, “Deepfake histologic images for enhancing digital pathology,” Laboratory Investigation , vol. 103, no. 1, p. 100006, 2023
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E. Adib, A. Fernandez, F. Afghah, and J. J. Prevost, “Synthetic ecg signal generation using probabilistic diffusion models,” 2023
2023
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H. Chung, J. Kim, J.-m. Kwon, K.-H. Jeon, M. S. Lee, and E. Choi, “Text-to-ecg: 12-lead electrocardiogram synthesis conditioned on clinical text reports,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Closest in time.