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\n  \n 2027\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n The Privacy–Utility Trade-Off in Brain MRI Synthesis: A Comparative Framework for Generative Models.\n \n \n \n\n\n \n Ninh, D. T.; Kien, N. D.; Huynh, T. N.; Anh, L. Q.; Hiep, D. T.; Manh, B. D.; Anh, N. H.; D'Haeseleer, M.; Van Schependom, J.; Denissen, S.; Long, T. Q.; Trung, N. L.; and Nagels, G.\n\n\n \n\n\n\n In Buntine, W.; Fjeld, M.; Schöffmann, K.; Tran The, T.; Tran, M.; Binh, H. T. T.; and Dam, T., editor(s), Information and Communication Technology, pages 398–412, Singapore, 2027. Springer Nature Singapore\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@InProceedings{10.1007/978-981-92-2600-9_31,\nauthor="Ninh, Dam Thai\nand Kien, Nguyen Duc\nand Huynh, Trinh Ngoc\nand Anh, Le Quoc\nand Hiep, Dinh Tran\nand Manh, Bui Duc\nand Anh, Nguyen Hai\nand D'Haeseleer, Miguel\nand Van Schependom, Jeroen\nand Denissen, Stijn\nand Long, Tran Quoc\nand Trung, Nguyen Linh\nand Nagels, Guy",\neditor="Buntine, Wray\nand Fjeld, Morten\nand Sch{\\"o}ffmann, Klaus\nand Tran The, Truyen\nand Tran, Minh-Triet\nand Binh, Huynh Thi Thanh\nand Dam, Tuan",\ntitle="The Privacy--Utility Trade-Off in Brain MRI Synthesis: A Comparative Framework for Generative Models",\nbooktitle="Information and Communication Technology",\nyear="2027",\npublisher="Springer Nature Singapore",\naddress="Singapore",\npages="398--412",\nabstract="Generative Adversarial Networks (GANs) showed significant potential for synthesizing realistic brain MRI scans. However, this capability introduced a critical risk of sensitive patient data leakage due to sample memorization. This study evaluated data leakage mitigation techniques for GANs applied to brain MRI synthesis, using 2D sagittal slices at 64{\\$}{\\$}{\\backslash}times {\\$}{\\$}{\\texttimes}64 resolution as a foundational experimental setup. We assessed a standard Deep Convolutional GAN (DCGAN) model and its variants incorporating four distinct data leakage mitigation strategies: Maximum Entropy GAN (MEGAN), Spectral Normalization (SN), Wasserstein GAN (WGAN) and Differential Privacy (DP). The models' efficacy was assessed based on their ability to reduce memorization and maintain image quality. We revealed that both MEGAN-SN and WGAN-SN-D provide an optimal balance, significantly reducing privacy risks while maintaining acceptable image quality. Conversely, DPGANs substantially compromised image quality to achieve their strong theoretical privacy guarantees.",\nisbn="978-981-92-2600-9"\n}\n\n\n
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\n Generative Adversarial Networks (GANs) showed significant potential for synthesizing realistic brain MRI scans. However, this capability introduced a critical risk of sensitive patient data leakage due to sample memorization. This study evaluated data leakage mitigation techniques for GANs applied to brain MRI synthesis, using 2D sagittal slices at 64$}{$\\times $}{$×64 resolution as a foundational experimental setup. We assessed a standard Deep Convolutional GAN (DCGAN) model and its variants incorporating four distinct data leakage mitigation strategies: Maximum Entropy GAN (MEGAN), Spectral Normalization (SN), Wasserstein GAN (WGAN) and Differential Privacy (DP). The models' efficacy was assessed based on their ability to reduce memorization and maintain image quality. We revealed that both MEGAN-SN and WGAN-SN-D provide an optimal balance, significantly reducing privacy risks while maintaining acceptable image quality. Conversely, DPGANs substantially compromised image quality to achieve their strong theoretical privacy guarantees.\n
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