Federated learning has received great attention for its capability to train a large-scale model in a decentralized manner without needing to access user data directly. It helps protect the users' private data from centralized collecting. Unlike distributed machine learning, federated learning aims to ...
First, FedSeg is a federated learning method that can learn from distributed data. Second, in this case, directly learning a segmentation model from real data is considered an upper bound of learning from synthetic data. Since the MM-DSL already achieved better results than the other GAN-based...
Furthermore, we also provide benchmark on center-wise under federated learning settings. Our dataset is public and can be downloaded at \url{ this https URL }. 展开 年份: 2024 收藏 引用 批量引用 报错 分享 全部来源 求助全文 arXiv.org 相似文献...
Hence many research networks such as OHDSI and PCORnet have adopted a federated model in which patient-level data are stored at local institutions and often only aggregated information are shared across sites5,6,7. Second, data from different sites are often heterogeneous with respect to patient ...
First, FedSeg is a federated learning method that can learn from distributed data. Second, in this case, directly learning a segmentation model from real data is considered an upper bound of learning from synthetic data. Since the MM-DSL already achieved better results than the other GAN-based...
The learning environment of a respective client node may include modules for the client device (or client node) to collaborate with the central learning system and other client nodes via a distributed learning (e.g., federated learning, split learning) framework. In one embodiment, the learning ...
Federated learningPersonalized modelingClusteringPersonalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent clients, e.g. smartphones or enterprises. FL can mitigate the data privacy risk ...
We present the first simulated federated learning study on the modality of cardiovascular magnetic resonance and use four centers derived from subsets of the M&M and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy. We adapt a 3D-CNN network pretrained on action recognition ...
Foundation models matter: federated learning for multi-center tuberculosis diagnosis via adaptive regularization and model-contrastive learningFederated learningFoundation modelsAdaptive regularizationContrastive learningTuberculosisIn tackling Tuberculosis (TB), a critical global health challenge, the integration of ...
Xie, MingAustralian AI Institute, University of Technology Sydney, Sydney, AustraliaShen, TaoAustralian AI Institute, University of Technology Sydney, Sydney, AustraliaZhou, TianyiUniversity of Maryland, Maryland, USAWang, XianzhiAustralian AI Institute, University of Technology Sydney, Sydney, Australia...