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Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary.

, , , , , , and . GoodIT, page 79-84. ACM, (2021)

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Observer study-based evaluation of TGAN architecture used to generate oncological PET images., , , , , , , , , and 2 other author(s). Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, volume 12467 of SPIE Proceedings, SPIE, (2023)Object Counting on Low Quality Images: A Case Study of Near Real-Time Traffic Monitoring., , , , and . ICMLA, page 1200-1204. IEEE, (2018)Private data sharing between decentralized users through the privGAN architecture., and . EDOC Workshops, page 37-42. IEEE, (2020)3-D PET Image Generation with tumour masks using TGAN., , , , , and . CoRR, (2021)3D PET image generation with tumour masks using TGAN., , , , , and . Medical Imaging: Image Processing, volume 12032 of SPIE Proceedings, SPIE, (2022)Assessing privacy leakage in synthetic 3-D PET imaging using transversal GAN., , , , and . Comput. Methods Programs Biomed., (January 2024)Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)., , and . NeurIPS, (2022)Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN., , , , and . CoRR, (2022)Group GAN., , and . CoRR, (2022)Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary., , , , , , and . GoodIT, page 79-84. ACM, (2021)