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Siloed Federated Learning for Multi-Centric Histopathology Datasets

Siloed Federated Learning for Multi-Centric Histopathology Datasets

17 August 2020
M. Andreux
Jean Ogier du Terrail
C. Béguier
Eric W. Tramel
    FedML
    OOD
    AI4CE
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Papers citing "Siloed Federated Learning for Multi-Centric Histopathology Datasets"

14 / 14 papers shown
Title
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated
  Semantic Segmentation for Autonomous Driving
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving
Eros Fani
Marco Ciccone
Barbara Caputo
FedML
16
4
0
23 Sep 2023
Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A
  Practical Review
Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review
Heather D. Couture
40
20
0
27 Nov 2022
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in
  Realistic Healthcare Settings
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail
Samy Ayed
Edwige Cyffers
Felix Grimberg
Chaoyang He
...
Sai Praneeth Karimireddy
Marco Lorenzi
Giovanni Neglia
Marc Tommasi
M. Andreux
FedML
38
142
0
10 Oct 2022
Federated Learning for Medical Applications: A Taxonomy, Current Trends,
  Challenges, and Future Research Directions
Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions
A. Rauniyar
D. Hagos
Debesh Jha
J. E. Haakegaard
Ulas Bagci
D. Rawat
Vladimir Vlassov
OOD
41
91
0
05 Aug 2022
Towards the Practical Utility of Federated Learning in the Medical
  Domain
Towards the Practical Utility of Federated Learning in the Medical Domain
Seongjun Yang
Hyeonji Hwang
Daeyoung Kim
Radhika Dua
Jong-Yeup Kim
Eunho Yang
E. Choi
FedML
OOD
13
15
0
07 Jul 2022
Towards Federated Long-Tailed Learning
Towards Federated Long-Tailed Learning
Zihan Chen
Songshan Liu
Hualiang Wang
Howard H. Yang
Tony Q. S. Quek
Zuozhu Liu
FedML
23
10
0
30 Jun 2022
Uncertainty Minimization for Personalized Federated Semi-Supervised
  Learning
Uncertainty Minimization for Personalized Federated Semi-Supervised Learning
Yanhang Shi
Siguang Chen
Haijun Zhang
FedML
21
8
0
05 May 2022
On the Pitfalls of Batch Normalization for End-to-End Video Learning: A
  Study on Surgical Workflow Analysis
On the Pitfalls of Batch Normalization for End-to-End Video Learning: A Study on Surgical Workflow Analysis
Dominik Rivoir
Isabel Funke
Stefanie Speidel
19
15
0
15 Mar 2022
FedDrive: Generalizing Federated Learning to Semantic Segmentation in
  Autonomous Driving
FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving
Lidia Fantauzzo
Eros Fani
Debora Caldarola
A. Tavera
Fabio Cermelli
Marco Ciccone
Barbara Caputo
FedML
21
52
0
28 Feb 2022
Federated Learning Challenges and Opportunities: An Outlook
Federated Learning Challenges and Opportunities: An Outlook
Jie Ding
Eric W. Tramel
Anit Kumar Sahu
Shuang Wu
Salman Avestimehr
Tao Zhang
FedML
33
55
0
01 Feb 2022
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on
  Heterogeneous Medical Images
HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images
Meirui Jiang
Zirui Wang
Qi Dou
FedML
19
123
0
20 Dec 2021
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Yangsibo Huang
Samyak Gupta
Zhao-quan Song
Kai Li
Sanjeev Arora
FedML
AAML
SILM
12
269
0
30 Nov 2021
FedDropoutAvg: Generalizable federated learning for histopathology image
  classification
FedDropoutAvg: Generalizable federated learning for histopathology image classification
G. N. Gunesli
M. Bilal
S. Raza
Nasir M. Rajpoot
FedML
OOD
14
20
0
25 Nov 2021
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning
  Convergence Analysis
FL-NTK: A Neural Tangent Kernel-based Framework for Federated Learning Convergence Analysis
Baihe Huang
Xiaoxiao Li
Zhao-quan Song
Xin Yang
FedML
23
16
0
11 May 2021
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