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Memory-aware curriculum federated learning for breast cancer
  classification

Memory-aware curriculum federated learning for breast cancer classification

6 July 2021
Amelia Jiménez-Sánchez
Mickael Tardy
M. A. G. Ballester
Diana Mateus
Gemma Piella
    OOD
ArXivPDFHTML

Papers citing "Memory-aware curriculum federated learning for breast cancer classification"

11 / 11 papers shown
Title
Federated Client-tailored Adapter for Medical Image Segmentation
Federated Client-tailored Adapter for Medical Image Segmentation
Guyue Hu
Siyuan Song
Yukun Kang
Z. Yin
Gangming Zhao
Chenglong Li
Jin Tang
FedML
MedIm
188
0
0
25 Apr 2025
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection
Yassine Habchi
Hamza Kheddar
Yassine Himeur
Adel Belouchrani
Erchin Serpedin
Fouad Khelifi
Muhammad E.H. Chowdhury
LM&MA
46
0
0
30 Mar 2025
Federated and Transfer Learning for Cancer Detection Based on Image
  Analysis
Federated and Transfer Learning for Cancer Detection Based on Image Analysis
Amine Bechar
Y. Elmir
Yassine Himeur
Rafik Medjoudj
Abbes Amira
MedIm
41
4
0
30 May 2024
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated
  Learning
Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning
Prakash Chourasia
Taslim Murad
Z. Tayebi
Sarwan Ali
Imdadullah Khan
Murray Patterson
FedML
34
5
0
17 Feb 2023
When Do Curricula Work in Federated Learning?
When Do Curricula Work in Federated Learning?
Saeed Vahidian
Sreevatsank Kadaveru
Woo-Ram Baek
Weijia Wang
Vyacheslav Kungurtsev
Cheng Chen
M. Shah
Bill Lin
FedML
40
11
0
24 Dec 2022
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in
  Medicine
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
A. Chaddad
Qizong Lu
Jiali Li
Y. Katib
R. Kateb
C. Tanougast
Ahmed Bouridane
Ahmed Abdulkadir
OOD
24
38
0
17 Nov 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
43
90
0
05 Aug 2022
FedBalancer: Data and Pace Control for Efficient Federated Learning on
  Heterogeneous Clients
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Jaemin Shin
Yuanchun Li
Yunxin Liu
Sung-Ju Lee
FedML
17
73
0
05 Jan 2022
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
787
0
15 Feb 2021
Towards Recognizing Unseen Categories in Unseen Domains
Towards Recognizing Unseen Categories in Unseen Domains
Massimiliano Mancini
Zeynep Akata
Elisa Ricci
Barbara Caputo
VLM
61
100
0
23 Jul 2020
The Future of Digital Health with Federated Learning
The Future of Digital Health with Federated Learning
Nicola Rieke
Jonny Hancox
Wenqi Li
Fausto Milletari
H. Roth
...
Ronald M. Summers
Andrew Trask
Daguang Xu
Maximilian Baust
M. Jorge Cardoso
OOD
174
1,707
0
18 Mar 2020
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