no code implementations • 24 Oct 2023 • Xiaofeng Liu, Thibault Marin, Tiss Amal, Jonghye Woo, Georges El Fakhri, Jinsong Ouyang
Specifically, we counteract the information loss in the forward process by introducing latent variables.
no code implementations • 17 Mar 2023 • Xiaofeng Liu, Thibault Marin, Tiss Amal, Jonghye Woo, Georges El Fakhri, Jinsong Ouyang
Purpose: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties.
no code implementations • 18 Jan 2022 • Xiaofeng Liu, Fangxu Xing, Thibault Marin, Georges El Fakhri, Jonghye Woo
Then, we apply a variational autoencoder network and optimize its evidence lower bound (ELBO) to efficiently approximate the distribution of the segmentation map, given an MR image.