ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2308.16376
  4. Cited By
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites:
  A Federated Learning Approach with Noise-Resilient Training

Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training

31 August 2023
Lei Bai
Dongang Wang
Michael Barnett
Mariano Cabezas
Weidong (Tom) Cai
Fernando Calamante
K. Kyle
Dongnan Liu
L. Ly
Aria Nguyen
C. Shieh
Ryan Sullivan
Hengrui Wang
Geng Zhan
Wanli Ouyang
Chenyu Wang
ArXivPDFHTML

Papers citing "Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training"

2 / 2 papers shown
Title
FedBN: Federated Learning on Non-IID Features via Local Batch
  Normalization
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization
Xiaoxiao Li
Meirui Jiang
Xiaofei Zhang
Michael Kamp
Qi Dou
OOD
FedML
168
770
0
15 Feb 2021
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for
  Automated Detection of Lacunes of Presumed Vascular Origin
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
Mohsen Ghafoorian
N. Karssemeijer
Tom Heskes
M. Bergkamp
Joost Wissink
...
K. Keizer
F.‐E. Leeuw
Bram van Ginneken
E. Marchiori
B. Platel
MedIm
207
124
0
24 Oct 2016
1