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Flamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings
J Ogier du Terrail, SS Ayed, E Cyffers… - Advances in …, 2022 - proceedings.neurips.cc
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …
A review of AI-based radiomics and computational pathology approaches in triple-negative breast cancer: current applications and perspectives
G Corredor, S Bharadwaj, T Pathak… - Clinical breast …, 2023 - Elsevier
Breast cancer is one of the most common and deadly cancers worldwide. Approximately,
20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is …
20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is …
FCA: taming long-tailed federated medical image classification by classifier anchoring
J Wicaksana, Z Yan, KT Cheng - ar** clinically robust deep learning models. Federated learning (FL) addresses the …
Non-parametric regularization for class imbalance federated medical image classification
J Wicaksana, Z Yan, KT Cheng - ar**
clinically robust deep learning models. Federated learning (FL) addresses the former by …
clinically robust deep learning models. Federated learning (FL) addresses the former by …
Centralized and Federated Heart Disease Classification Models Using UCI Dataset and their Shapley-value Based Interpretability
MP Rodriguez, M Nafea - arxiv preprint arxiv:2408.06183, 2024 - arxiv.org
Cardiovascular diseases are a leading cause of mortality worldwide, highlighting the need
for accurate diagnostic methods. This study benchmarks centralized and federated machine …
for accurate diagnostic methods. This study benchmarks centralized and federated machine …
Tackling heterogeneity in federated learning systems
O Marfoq - 2023 - theses.hal.science
Federated Learning (FL) stands as a framework facilitating geographically distributed clients
to collaboratively learn machine learning models without divulging their local data. This …
to collaboratively learn machine learning models without divulging their local data. This …
A Comprehensive Survey on Federated Learning and its Applications in Health Care
SAB Pacheco - 2024 IEEE International Conference on Artificial …, 2024 - ieeexplore.ieee.org
With the advancements in the Internet of Things (IoT), data collection has become easier
and quicker than ever. Data collected from various devices and organizations leads to the …
and quicker than ever. Data collected from various devices and organizations leads to the …
A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets
F Tavakoli, DB Emerson, S Ayromlou, J Jewell… - arxiv preprint arxiv …, 2023 - arxiv.org
Federated learning (FL) is increasingly being recognized as a key approach to overcoming
the data silos that so frequently obstruct the training and deployment of machine-learning …
the data silos that so frequently obstruct the training and deployment of machine-learning …
Building Blocks to Address Variations in Federated Medical Image Analysis
J Wicaksana - 2024 - search.proquest.com
Federated learning (FL) has shown promising potential in enabling multiple medical
institutions/clients to collaboratively train deep learning models while preserving data …
institutions/clients to collaboratively train deep learning models while preserving data …
Neural network for the prediction of treatment response in Triple Negative Breast Cancer *
P Naylor, T Lazard, G Bataillon, M Lae… - bioRxiv, 2022 - biorxiv.org
The automatic analysis of stained histological sections is becoming increasingly popular.
Deep Learning is today the method of choice for the computational analysis of such data …
Deep Learning is today the method of choice for the computational analysis of such data …