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Federated learning for medical imaging radiology
MHU Rehman, W Hugo Lopez Pinaya… - The British Journal of …, 2023 - academic.oup.com
Federated learning (FL) is gaining wide acceptance across the medical AI domains. FL
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …
promises to provide a fairly acceptable clinical-grade accuracy, privacy, and generalisability …
Revisiting weighted aggregation in federated learning with neural networks
In federated learning (FL), weighted aggregation of local models is conducted to generate a
global model, and the aggregation weights are normalized (the sum of weights is 1) and …
global model, and the aggregation weights are normalized (the sum of weights is 1) and …
Nvidia flare: Federated learning from simulation to real-world
HR Roth, Y Cheng, Y Wen, I Yang, Z Xu… - ar** and deploying deep learning models in brain magnetic resonance imaging: A review
Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to
alleviate the burden on radiologists and MR technologists, and improve throughput. The …
alleviate the burden on radiologists and MR technologists, and improve throughput. The …
No fear of classifier biases: Neural collapse inspired federated learning with synthetic and fixed classifier
Data heterogeneity is an inherent challenge that hinders the performance of federated
learning (FL). Recent studies have identified the biased classifiers of local models as the key …
learning (FL). Recent studies have identified the biased classifiers of local models as the key …
Resource-adaptive federated learning with all-in-one neural composition
Abstract Conventional Federated Learning (FL) systems inherently assume a uniform
processing capacity among clients for deployed models. However, diverse client hardware …
processing capacity among clients for deployed models. However, diverse client hardware …
A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening …
Background Multicentre training could reduce biases in medical artificial intelligence (AI);
however, ethical, legal, and technical considerations can constrain the ability of hospitals to …
however, ethical, legal, and technical considerations can constrain the ability of hospitals to …
One model to unite them all: Personalized federated learning of multi-contrast MRI synthesis
Curation of large, diverse MRI datasets via multi-institutional collaborations can help
improve learning of generalizable synthesis models that reliably translate source-onto target …
improve learning of generalizable synthesis models that reliably translate source-onto target …