1 citations · 1 across the 3 of their papers we have counts for
3 papers
eess.IV2023
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Lei Bai, Dongang Wang, Michael Barnett +13
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct ther…
eess.IV2022★ 1 cited
MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning
Dongnan Liu, Mariano Cabezas, Dongang Wang +16
Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's…
physics.med-ph2018
Learning from our neighbours: a novel approach on sinogram completion using bin-sharing and deep learning to reconstruct high quality 4DCBCT
Joel Beaudry, Pedro L. Esquinas
Inspired by the success of deep learning applications on restoration of low-dose and sparse CT images, we propose a novel method to reconstruct high-quality 4D cone-beam CT (4DCBCT…