W. N. Price and I. G. Cohen, “Privacy in the age of medical big data,” Nat. Med., vol. 25, no. 1, pp. 37–43, Jan. 2019, https://doi.org/10.1038/s41591-018-0272-7.
Article PubMed PubMed Central CAS Google Scholar
P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Haworth, “A review of medical image data augmentation techniques for deep learning applications,” J. Med. Imaging Radiat. Oncol., vol. 65, no. 5, Art. no. 5, 2021, https://doi.org/10.1111/1754-9485.13261.
M. Giuffrè and D. L. Shung, “Harnessing the power of synthetic data in healthcare: innovation, application, and privacy,” Npj Digit. Med., vol. 6, no. 1, pp. 1–8, Oct. 2023, https://doi.org/10.1038/s41746-023-00927-3.
A. Gonzales, G. Guruswamy, and S. R. Smith, “Synthetic data in health care: A narrative review,” PLOS Digit. Health, vol. 2, no. 1, p. e0000082, Jan. 2023, https://doi.org/10.1371/journal.pdig.0000082.
Article PubMed PubMed Central Google Scholar
L. R. Koetzier et al., “Generating Synthetic Data for Medical Imaging,” Radiology, vol. 312, no. 3, p. e232471, Sep. 2024, https://doi.org/10.1148/radiol.232471.
E. Sizikova et al., “Synthetic data in radiological imaging: current state and future outlook,” BJRArtificial Intell., vol. 1, no. 1, p. ubae007, Mar. 2024, https://doi.org/10.1093/bjrai/ubae007.
B. van Breugel, T. Liu, D. Oglic, and M. van der Schaar, “Synthetic data in biomedicine via generative artificial intelligence,” Nat. Rev. Bioeng. 2, 991–1004 (2024), https://doi.org/10.1038/s44222-024-00245-7
T. E. Raghunathan, “Synthetic Data,” Annu. Rev. Stat. Its Appl., vol. 8, no. Volume 8, 2021, pp. 129–140, Mar. 2021, https://doi.org/10.1146/annurev-statistics-040720-031848.
I. Goodfellow et al., “Generative Adversarial Nets,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2014. Accessed: May 19, 2023. [Online]. Available: https://papers.nips.cc/paper_files/paper/2014/file/f033ed80deb0234979a61f95710dbe25-Paper.pdf
J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2020, pp. 6840–6851. Accessed: Jun. 25, 2024. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html
G. Somepalli, V. Singla, M. Goldblum, J. Geiping, and T. Goldstein, “Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models,” in 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023, pp. 6048–6058. https://doi.org/10.1109/CVPR52729.2023.00586.
N. Carlini, J. Hayes, M. Nasr, M. Jagielski, V. Sehwag, F. Tramèr, B. Balle, D. Ippolito, and E. Wallace, “Extracting Training Data from Diffusion Models,”. Published online Jan. 30, 2023. Accessed: Feb. 19, 2024. [Online]. https://doi.org/10.48550/arXiv.2301.13188
L. Wang, W. Chen, W. Yang, F. Bi, and F. R. Yu, “A State-of-the-Art Review on Image Synthesis With Generative Adversarial Networks,” IEEE Access, vol. 8, pp. 63514–63537, 2020, https://doi.org/10.1109/ACCESS.2020.2982224.
S. Kazeminia et al., “GANs for medical image analysis,” Artif. Intell. Med., vol. 109, p. 101938, Sep. 2020, https://doi.org/10.1016/j.artmed.2020.101938.
T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila, “Training Generative Adversarial Networks with Limited Data,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2020, pp. 12104–12114. Accessed: Jul. 11, 2023. [Online]. Available: https://papers.nips.cc/paper/2020/hash/8d30aa96e72440759f74bd2306c1fa3d-Abstract.html
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive Growing of GANs for Improved Quality, Stability, and Variation,” ArXiv171010196 Cs Stat, Feb. 2018, Accessed: Feb. 07, 2022. [Online]. Available: http://arxiv.org/abs/1710.10196
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and Improving the Image Quality of StyleGAN,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 8107–8116. https://doi.org/10.1109/CVPR42600.2020.00813.
M. Kang, J. Shin, and J. Park, “StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 12, pp. 15725–15742, Dec. 2023, https://doi.org/10.1109/TPAMI.2023.3306436.
M. Woodland. J. Wood, B. M. Anderson, S. Kundu, E. Lin, E. Koay, B. Odisio, C. Chung, H. C. Kang, A. M. Venkatesan, S. Yedururi, B. De, Y. Lin, A. B. Patel, and K. K. Brock, “Evaluating the Performance of StyleGAN2-ADA on Medical Images,”. In: Zhao, C., Svoboda, D., Wolterink, J.M., Escobar, M. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2022. Lecture Notes in Computer Science, vol 13570. Springer, Cham. https://doi.org/10.1007/978-3-031-16980-9_14
A. Montero, E. Bonet-Carne, and X. P. Burgos-Artizzu, “Generative Adversarial Networks to Improve Fetal Brain Fine-Grained Plane Classification,” Sensors, vol. 21, no. 23, Art. no. 23, Jan. 2021, https://doi.org/10.3390/s21237975.
S. Yang, K.-D. Kim, E. Ariji, N. Takata, and Y. Kise, “Evaluating the performance of generative adversarial network-synthesized periapical images in classifying C-shaped root canals,” Sci. Rep., vol. 13, no. 1, Art. no. 1, Oct. 2023, https://doi.org/10.1038/s41598-023-45290-1.
L. Fetty et al., “Latent space manipulation for high-resolution medical image synthesis via the StyleGAN,” Z. Für Med. Phys., vol. 30, no. 4, pp. 305–314, Nov. 2020, https://doi.org/10.1016/j.zemedi.2020.05.001.
J.-N. Eckardt et al., “Synthetic bone marrow images augment real samples in developing acute myeloid leukemia microscopy classification models,” Npj Digit. Med., vol. 8, no. 1, pp. 1–10, Mar. 2025, https://doi.org/10.1038/s41746-025-01563-9.
B. Muñoz, R. Pezoa, and H. Gutierrez, “Histopathology Image Augmentation Through StyleGAN2-ADA,”. In: Guerrero, G., San Martín, J., Meneses, E., Barrios Hernández, C.J., Osthoff, C., Monsalve Diaz, J.M. (eds) High Performance Computing. CARLA 2024. Communications in Computer and Information Science, vol 2270. Springer, Cham. https://doi.org/10.1007/978-3-031-80084-9_15
D. Wang et al., “Improving Artificial Intelligence–based Microbial Keratitis Screening Tools Constrained by Limited Data Using Synthetic Generation of Slit-Lamp Photos,” Ophthalmol. Sci., vol. 5, no. 3, p. 100676, May 2025, https://doi.org/10.1016/j.xops.2024.100676.
R. Yasuda, A. Sugawara, and H. Nishi, “Unsupervised Anomaly Sound Analysis Method Using StyleGAN2 to Estimate the Degree and Type of Abnormalities,” in IECON 2024 - 50th Annual Conference of the IEEE Industrial Electronics Society, Chicago, IL, USA, 2024, pp. 1–6. https://doi.org/10.1109/IECON55916.2024.10905269.
M. Zoghby, B. Erickson, and G. Conte, “Generative Adversarial Networks for Brain MRI Synthesis: Impact of Training Set Size on Clinical Application,” J. Imaging Inform. Med., vol. 37, no. 3, pp. 1228–1238, Jun. 2024, https://doi.org/10.1007/s10278-024-00976-4.
Article PubMed PubMed Central CAS Google Scholar
E. Dikici, M. Bigelow, R. D. White, B. S. Erdal, and L. M. Prevedello, “Constrained generative adversarial network ensembles for sharable synthetic medical images,” J. Med. Imaging, vol. 8, no. 2, p. 024004, Mar. 2021, https://doi.org/10.1117/1.JMI.8.2.024004.
T. Kossen et al., “Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks,” Comput. Biol. Med., vol. 131, p. 104254, Apr. 2021, https://doi.org/10.1016/j.compbiomed.2021.104254.
A. Borji, “Pros and cons of GAN evaluation measures,” Comput. Vis. Image Underst., vol. 179, pp. 41–65, Feb. 2019, https://doi.org/10.1016/j.cviu.2018.10.009.
A. Borji, “Pros and cons of GAN evaluation measures: New developments,” Comput. Vis. Image Underst., vol. 215, p. 103329, Jan. 2022, https://doi.org/10.1016/j.cviu.2021.103329.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, Apr. 2004, https://doi.org/10.1109/TIP.2003.819861.
H. Ali et al., “The role of generative adversarial networks in brain MRI: a scoping review,” Insights Imaging, vol. 13, no. 1, p. 98, Jun. 2022, https://doi.org/10.1186/s13244-022-01237-0.
Article PubMed PubMed Central Google Scholar
S. Hu, B. Lei, S. Wang, Y. Wang, Z. Feng, and Y. Shen, “Bidirectional Mapping Generative Adversarial Networks for Brain MR to PET Synthesis,” IEEE Trans. Med. Imaging, vol. 41, no. 1, pp. 145–157, Jan. 2022, https://doi.org/10.1109/TMI.2021.3107013.
J. Zhu, C. Tan, J. Yang, G. Yang, and P. Lio’, “Arbitrary Scale Super-Resolution for Medical Images,” Int. J. Neural Syst., vol. 31, no. 10, p. 2150037, Oct. 2021, https://doi.org/10.1142/S0129065721500374.
W.-J. Do, S. Seo, Y. Han, J. C. Ye, S. H. Choi, and S.-H. Park, “Reconstruction of multicontrast MR images through deep learning,” Med. Phys., vol. 47, no. 3, pp. 983–997, 2020, https://doi.org/10.1002/mp.14006.
B. Dufumier, A. Grigis, J. Victor, C. Ambroise, V. Frouin, and E. Duchesnay, “OpenBHB: a Large-Scale Multi-Site Brain MRI Data-set for Age Prediction and Debiasing,” NeuroImage, vol. 263, p. 119637, Nov. 2022, https://doi.org/10.1016/j.neuroimage.2022.119637.
B. B. Avants, N. Tustison, and H. Johnson, “Advanced Normalization Tools (ANTS)”. Insight J. 2, 1–35 (2009). http://hdl.handle.net/10380/3113
M. Jenkinson and S. Smith, “A global optimisation method for robust affine registration of brain images,” Med. Image Anal., vol. 5, no. 2, pp. 143–156, Jun. 2001, https://doi.org/10.1016/S1361-8415(01)00036-6.
Article PubMed CAS Google Scholar
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, Curran Associates, Inc., 2017. Accessed: Feb. 09, 2022. [Online]. Available: https://proceedings.neurips.cc/paper/2017/hash/8a1d694707eb0fefe65871369074926d-Abstract.html
M. Bińkowski, D. J. Sutherland, M. Arbel, and A. Gretton, “Demystifying MMD GANs,” presented at the International Conference on Learning Representations, Feb. 2022. Accessed: Jan. 17, 2023. [Online]. Available: https://openreview.net/forum?id=r1lUOzWCW
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila, “Improved Precision and Recall Metric for Assessing Generative Models,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2019. Accessed: Nov. 02, 2023. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2019/hash/0234c510bc6d908b28c70ff313743079-Abstract.html
M. F. Naeem, S. J. Oh, Y. Uh, Y. Choi, and J. Yoo, “Reliable Fidelity and Diversity Metrics for Generative Models”. in Proc. Int. Conf. Mach. Learn., Nov. 2020, pp. 7176
Comments (0)