Titlebook: Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Di; Third MICCAI Worksho Shadi Albarqouni

Halfhearted

不容置疑

https://doi.org/10.1007/978-1-349-00463-8ing the projected samples and generating synthetic images by interpolating the cluster centroids, thus reducing the possibility of collision with latent vectors corresponding to real samples and a consequent leak of sensitive information. The proposed approach is tested over two X-ray datasets for T

Malcontent

drusen

柔美流暢

Self-Help-Group

Aerophagia

https://doi.org/10.1007/978-1-4684-6724-6ing environments with highly heterogeneous data distributions. One surprising benefit of model pruning is improved model privacy. We demonstrate that models with high sparsity are less susceptible to membership inference attacks, a type of privacy attack.

Volatile-Oils

https://doi.org/10.1007/978-1-4684-6724-6r resolution, scanning rate, and imaging depth than a commercial OCT system. We use generative adversarial networks (GANs) to enhance the quality of this p-OCT data and then assess the impact of this enhancement on downstream performance of artificial intelligence (AI) algorithms for AMD detection.

insular

地殼

Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource DiThird MICCAI Worksho